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pages: 447 words: 104,258

Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues by Alain Ruttiens

algorithmic trading, asset allocation, asset-backed security, backtesting, banking crisis, Black Swan, Black-Scholes formula, Brownian motion, capital asset pricing model, collateralized debt obligation, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discounted cash flows, discrete time, diversification, fixed income, implied volatility, interest rate derivative, interest rate swap, margin call, market microstructure, martingale, p-value, passive investing, quantitative trading / quantitative finance, random walk, risk/return, Satyajit Das, Sharpe ratio, short selling, statistical model, stochastic process, stochastic volatility, time value of money, transaction costs, value at risk, volatility smile, Wiener process, yield curve, zero-coupon bond

Contents Cover Series Title Page Copyright Dedication Foreword Main Notations Introduction Part I: The Deterministic Environment Chapter 1: Prior to the yield curve: spot and forward rates 1.1 INTEREST RATES, PRESENT AND FUTURE VALUES, INTEREST COMPOUNDING 1.2 DISCOUNT FACTORS 1.3 CONTINUOUS COMPOUNDING AND CONTINUOUS RATES 1.4 FORWARD RATES 1.5 THE NO ARBITRAGE CONDITION FURTHER READING Chapter 2: The term structure or yield curve 2.1 INTRODUCTION TO THE YIELD CURVE 2.2 THE YIELD CURVE COMPONENTS 2.3 BUILDING A YIELD CURVE: METHODOLOGY 2.4 AN EXAMPLE OF YIELD CURVE POINTS DETERMINATION 2.5 INTERPOLATIONS ON A YIELD CURVE FURTHER READING Chapter 3: Spot instruments 3.1 SHORT-TERM RATES 3.2 BONDS 3.3 CURRENCIES FURTHER READING Chapter 4: Equities and stock indexes 4.1 STOCKS VALUATION 4.2 STOCK INDEXES 4.3 THE PORTFOLIO THEORY FURTHER READING Chapter 5: Forward instruments 5.1 THE FORWARD FOREIGN EXCHANGE 5.2 FRAs 5.3 OTHER FORWARD CONTRACTS 5.4 CONTRACTS FOR DIFFERENCE (CFD) FURTHER READING Chapter 6: Swaps 6.1 DEFINITIONS AND FIRST EXAMPLES 6.2 PRIOR TO AN IRS SWAP PRICING METHOD 6.3 PRICING OF AN IRS SWAP 6.4 (RE)VALUATION OF AN IRS SWAP 6.5 THE SWAP (RATES) MARKET 6.6 PRICING OF A CRS SWAP 6.7 PRICING OF SECOND-GENERATION SWAPS FURTHER READING Chapter 7: Futures 7.1 INTRODUCTION TO FUTURES 7.2 FUTURES PRICING 7.3 FUTURES ON EQUITIES AND STOCK INDEXES 7.4 FUTURES ON SHORT-TERM INTEREST RATES 7.5 FUTURES ON BONDS 7.6 FUTURES ON CURRENCIES 7.7 FUTURES ON (NON-FINANCIAL) COMMODITIES FURTHER READING Part II: The Probabilistic Environment Chapter 8: The basis of stochastic calculus 8.1 STOCHASTIC PROCESSES 8.2 THE STANDARD WIENER PROCESS, OR BROWNIAN MOTION 8.3 THE GENERAL WIENER PROCESS 8.4 THE ITÔ PROCESS 8.5 APPLICATION OF THE GENERAL WIENER PROCESS 8.6 THE ITÔ LEMMA 8.7 APPLICATION OF THE ITô LEMMA 8.8 NOTION OF RISK NEUTRAL PROBABILITY 8.9 NOTION OF MARTINGALE ANNEX 8.1: PROOFS OF THE PROPERTIES OF dZ(t) ANNEX 8.2: PROOF OF THE ITÔ LEMMA FURTHER READING Chapter 9: Other financial models: from ARMA to the GARCH family 9.1 THE AUTOREGRESSIVE (AR) PROCESS 9.2 THE MOVING AVERAGE (MA) PROCESS 9.3 THE AUTOREGRESSION MOVING AVERAGE (ARMA) PROCESS 9.4 THE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) PROCESS 9.5 THE ARCH PROCESS 9.6 THE GARCH PROCESS 9.7 VARIANTS OF (G)ARCH PROCESSES 9.8 THE MIDAS PROCESS FURTHER READING Chapter 10: Option pricing in general 10.1 INTRODUCTION TO OPTION PRICING 10.2 THE BLACK–SCHOLES FORMULA 10.3 FINITE DIFFERENCE METHODS: THE COX–ROSS–RUBINSTEIN (CRR) OPTION PRICING MODEL 10.4 MONTE CARLO SIMULATIONS 10.5 OPTION PRICING SENSITIVITIES FURTHER READING Chapter 11: Options on specific underlyings and exotic options 11.1 CURRENCY OPTIONS 11.2 OPTIONS ON BONDS 11.3 OPTIONS ON INTEREST RATES 11.4 EXCHANGE OPTIONS 11.5 BASKET OPTIONS 11.6 BERMUDAN OPTIONS 11.7 OPTIONS ON NON-FINANCIAL UNDERLYINGS 11.8 SECOND-GENERATION OPTIONS, OR EXOTICS FURTHER READING Chapter 12: Volatility and volatility derivatives 12.1 PRACTICAL ISSUES ABOUT THE VOLATILITY 12.2 MODELING THE VOLATILITY 12.3 REALIZED VOLATILITY MODELS 12.4 MODELING THE CORRELATION 12.5 VOLATILITY AND VARIANCE SWAPS FURTHER READING Chapter 13: Credit derivatives 13.1 INTRODUCTION TO CREDIT DERIVATIVES 13.2 VALUATION OF CREDIT DERIVATIVES 13.3 CONCLUSION FURTHER READING Chapter 14: Market performance and risk measures 14.1 RETURN AND RISK MEASURES 14.2 VaR OR VALUE-AT-RISK FURTHER READING Chapter 15: Beyond the Gaussian hypothesis: potential troubles with derivatives valuation 15.1 ALTERNATIVES TO THE GAUSSIAN HYPOTHESIS 15.2 POTENTIAL TROUBLES WITH DERIVATIVES VALUATION FURTHER READING Bibliography Index For other titles in the Wiley Finance series please see www.wiley.com/finance This edition first published 2013 Copyright © 2013 Alain Ruttiens Registered office John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com.

As a result, except for the USD yield curve, market practitioners prefer to start from a swap yield curve and, for each maturity, deduct some spread to obtain the corresponding risk-free yield curve, or add a spread to quote corporate bonds of other issuers of lower rating, or to penalize a restricted liquidity. However, the market nowadays tends to favor a variant of the swap curve called the OIS swap curve (OIS swaps are explained in Chapter 6, Section 6.7.2). In addition, we will see that interpolating rates between two points of a yield curve is much easier and grounded on a swap curve than on a government bonds yield curve. We will therefore present the building of both yield curves, but in more detail for the swap curve. Theoretically, interest rates as data points may form a yield curve in various ways.

Swaps and swap rates are studied in Chapter 6. 2. Yield curves are studied in Chapter 2. Here we just compare “rough” curves of joined discount factors and of zeroes. 3. Although in the practice, the minimum period for an interest period is a day. 4. In some cases, the reasoning is unfortunately not possible, for example, with credit derivatives. The valuation of such instruments is, therefore, more questionable. 2 The term structure or yield curve 2.1 INTRODUCTION TO THE YIELD CURVE A term structure or yield curve can be defined as the graph of spot rates or zeroes1 in function of their maturity. Since most of the time interest rates are higher with longer maturities, one talks of a “normal” yield curve if it is going upwards, and of an “inverse” yield curve if and when longer rates are lower than shorter rates.


pages: 1,202 words: 424,886

Stigum's Money Market, 4E by Marcia Stigum, Anthony Crescenzi

accounting loophole / creative accounting, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Black-Scholes formula, Brownian motion, business climate, buy and hold, capital controls, central bank independence, centralized clearinghouse, corporate governance, credit crunch, Credit Default Swap, currency manipulation / currency intervention, David Ricardo: comparative advantage, disintermediation, distributed generation, diversification, diversified portfolio, financial innovation, financial intermediation, fixed income, full employment, high net worth, implied volatility, income per capita, intangible asset, interest rate derivative, interest rate swap, large denomination, locking in a profit, London Interbank Offered Rate, margin call, market bubble, market clearing, market fundamentalism, money market fund, mortgage debt, Myron Scholes, offshore financial centre, paper trading, pension reform, Ponzi scheme, price mechanism, price stability, profit motive, Real Time Gross Settlement, reserve currency, risk tolerance, risk/return, seigniorage, shareholder value, short selling, technology bubble, the payments system, too big to fail, transaction costs, two-sided market, value at risk, volatility smile, yield curve, zero-coupon bond, zero-sum game

The few examples shown above clearly suggest that the yield curve truly is the bond market’s equivalent of a crystal ball. And it’s a tool that’s so simple to use that just about anyone can use it. Why the Treasury Yield Curve? The Treasury yield curve is by far the most closely followed yield curve. It is the first yield curve that market participants and forecasters look to for signals about the economy and the financial markets. There are two main reasons for this. First, because Treasuries are not at risk of default, the Treasury yield curve provides a “clean” look at where market participants believe interest rates should be along the various maturities. Unlike other yield curves such as the yield curve on corporate bonds, the Treasury yield curve is not distorted by differences in creditworthiness.

(For an example of a basis trade, see Chapter 16.) THE YIELD CURVE: A CRYSTAL BALL? Investors always seem to be looking for a crystal ball to help them predict the future. In the bond market, there’s one indicator that many investors put ahead of all the rest: the yield curve. It’s the closest thing that the bond market has to a crystal ball. For decades the yield curve has reliably foreshadowed major events and turning points in both the financial markets and the economy. For these reasons, the yield curve is one of the most closely watched financial indicators. Before we go on, let us tell you a little bit about what the yield curve is. For simplicity’s sake, assume that when we say “yield curve,” we are talking about the yield curve for U.S. Treasuries. The yield curve is a chart that plots the yield on bonds against their maturities.

Moreover, when drawing a yield curve for securities other than Treasuries, choosing the specific security to place on the yield curve becomes a subjective decision. For example, deciding which corporate bonds to use in a yield curve on corporate bonds requires choosing between numerous different companies. Largely for these three reasons, it’s best to stick with the yield curve on Treasury securities to get the most accurate reflection of market sentiment and the most reliable signals on the outlook for both the economy and the financial markets. Trading the Yield Curve Traders have always understood that there is a difference between the risk that the yield curve might shift up or down and the risk that the relationship between two yields along the yield curve might change. Traders position themselves to profit from the first sort of risk and do arbitrages to profit from the second (recall Chapter 10).


pages: 1,088 words: 228,743

Expected Returns: An Investor's Guide to Harvesting Market Rewards by Antti Ilmanen

Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, Bernie Madoff, Black Swan, Bretton Woods, business cycle, buy and hold, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, central bank independence, collateralized debt obligation, commoditize, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, G4S, George Akerlof, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, information asymmetry, interest rate swap, invisible hand, Kenneth Rogoff, laissez-faire capitalism, law of one price, London Interbank Offered Rate, Long Term Capital Management, loss aversion, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, Myron Scholes, negative equity, New Journalism, oil shock, p-value, passive investing, Paul Samuelson, performance metric, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, Robert Shiller, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, stocks for the long run, survivorship bias, systematic trading, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs, tulip mania, value at risk, volatility arbitrage, volatility smile, working-age population, Y2K, yield curve, zero-coupon bond, zero-sum game

The implications of two hypotheses about yield curve behavior Pure expectations hypothesis Risk premium hypothesis What is the information in forward rates (yield curve steepness)? Market’s rate expectations Required bond risk premia What future events should forward rates forecast? Future interest rate changes Near-term return differentials across bonds What is the best predictor of a 5-year zero-coupon bond’s 1-year return? The 1-year riskless spot rate The 5-year zero’s “rolling yield” (which is also the 1-year forward rate after 4 years) What is the best predictor of next year’s spot yield curve? Implied spot yield curve one year forward Current spot yield curve Roll or slide is another nuanced aspect of carry. The random walk hypothesis assumes that the current yield curve is the best predictor of the future yield curve.

The rate expectation component can be expressed either in terms of expected multi-year changes in the 1-year yield over the next decade or, alternatively, as the expected next-year change in the 10-year yield, scaled by its (end-of-horizon) duration. The first yield curve equation focuses on gradual changes in short rates and the yield-based BRPY while the second equation focuses on near-term changes in long yields and the return-based BRPH. Alternative theories Which of the two components has a larger influence on the yield curve shape? To interpret the yield curve, one can usefully contrast the classic pure expectations hypothesis (PEH) with the random walk hypothesis. The PEH makes the extreme assumption that risk premia are zero and is consistent with the idea of investor risk neutrality. One can then virtually read the market’s rate expectations off the yield curve (specifically, off the forward rate curve). Suppose a particularly steep yield curve indicates that, according to the PEH, the market expects short rates to rise quickly over time (to exactly offset longer bonds’ initial yield advantage; thus all bond investments have the same expected return).

The random walk hypothesis assumes that the current yield curve is the best predictor of the future yield curve. If an upward-sloping yield curve remains unchanged over the next year, a long-term bond’s yield income advantage over the one-year bond will be augmented by capital gains through a “rolldown” effect. For example, if the current 4-year rate is 20 bp lower than the current 5-year rate, the assumption of an unexpected yield curve implies that the bond’s yield will fall by 20 bp simply as a result of aging and rolling down the yield curve. The consequent “rolldown return” gives roughly a 0.8% capital gain (a 4-year duration times a 20 bp yield decline) even if the constant maturity yield curve is unchanged. We can calculate a broader carry measure that incorporates both yield income and rolldown return; it is called “rolling yield” and reflects return in an unchanged curve scenario.


The Global Money Markets by Frank J. Fabozzi, Steven V. Mann, Moorad Choudhry

asset allocation, asset-backed security, bank run, Bretton Woods, buy and hold, collateralized debt obligation, credit crunch, discounted cash flows, discrete time, disintermediation, fixed income, high net worth, intangible asset, interest rate derivative, interest rate swap, large denomination, locking in a profit, London Interbank Offered Rate, Long Term Capital Management, margin call, market fundamentalism, money market fund, moral hazard, mortgage debt, paper trading, Right to Buy, short selling, stocks for the long run, time value of money, value at risk, Y2K, yield curve, zero-coupon bond, zero-sum game

This risk is defined as any mismatch between adjustments to the coupon rate paid to bondholders and the interest rate paid on the floating-rate collateral. Two common sources of basis risk are index risk and reset risk. Index risk is a type of yield curve risk that arises because the ABS floater’s coupon rate and the interest rate of the underlying collateral are usually determined at different ends of the yield curve. Specifically, the floater’s coupon rate is typically spread off the short-term sector of the yield curve (e.g., U.S. Treasury) while the collateral’s interest rate is spread off a longer maturity sector of the same yield curve or in some cases a different yield curve (e.g., LIBOR). This mismatch is a source of risk. For example, for home equity loan-backed securities in which the collateral is adjustable-rate loans, the reference rate for the loans may be 6-month LIBOR while the reference rate for the bonds is usually 1month LIBOR.

For example, for home equity loan-backed securities in which the collateral is adjustable-rate loans, the reference rate for the loans may be 6-month LIBOR while the reference rate for the bonds is usually 1month LIBOR. Both the collateral and the bonds are indexed off LIBOR, but different sectors of the Eurodollar yield curve. The reference rate for some home equity loans is a constant maturity Treasury. Thus, the collateral is based on a spread off the 1-month sector of the Eurodollar yield curve while the bonds are spread off a longer maturity sector of the Treasury yield curve. As another example, for credit cardbacked ABS the interest rate paid is usually a spread over the prime rate (a spread over the Treasury yield curve) while the coupon rate for the bonds is usually a spread over 1-month LIBOR (a spread over the Eurodollar yield curve). Reset risk is the risk associated with the mismatch between the frequency of the resetting of the interest rate on the floating-rate collateral and the frequency of reset of the coupon rate on the bonds.

Cook, “Treasury Bills,” in Instruments of the Money Market, Seventh Edition, (Richmond: Federal Reserve Bank of Richmond), pp. 75–88. 36 THE GLOBAL MONEY MARKETS Because of LIBOR’s importance in the global money markets, it is instructive to examine the relationship between Treasury bill yields and LIBOR. We expect LIBOR rates to be higher than the yields on bills of the same maturity because investors in Eurodollars CDs are exposed to default risk. Panel a of Exhibit 3.6 presents a Bloomberg graph of the yield curves for U.S. Treasury bills and LIBOR (out to a maturity of 1 year) on March 13, 2002. The Treasury bill yield curve is the lower curve and is represented by a solid black line. Panel b of the exhibit presents the data used in constructing the two yield curves. The fourth column indicates the spread between LIBOR and the Treasury bill yield for a given maturity. In order to understand the relationship between LIBOR and Treasury bill yields over time, we examine the period January 1, 1987 to December 31, 1999. We focus on the spread (in basis points) between 3-month LIBOR and 3-month Treasury yields each week (Friday) during this time period.


pages: 504 words: 139,137

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined by Lasse Heje Pedersen

activist fund / activist shareholder / activist investor, algorithmic trading, Andrei Shleifer, asset allocation, backtesting, bank run, banking crisis, barriers to entry, Black-Scholes formula, Brownian motion, business cycle, buy and hold, buy low sell high, capital asset pricing model, commodity trading advisor, conceptual framework, corporate governance, credit crunch, Credit Default Swap, currency peg, David Ricardo: comparative advantage, declining real wages, discounted cash flows, diversification, diversified portfolio, Emanuel Derman, equity premium, Eugene Fama: efficient market hypothesis, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, Gordon Gekko, implied volatility, index arbitrage, index fund, interest rate swap, late capitalism, law of one price, Long Term Capital Management, margin call, market clearing, market design, market friction, merger arbitrage, money market fund, mortgage debt, Myron Scholes, New Journalism, paper trading, passive investing, price discovery process, price stability, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, shareholder value, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stocks for the long run, stocks for the long term, survivorship bias, systematic trading, technology bubble, time value of money, total factor productivity, transaction costs, value at risk, Vanguard fund, yield curve, zero-coupon bond

• Bond carry trade: A bond’s carry is its yield-to-maturity in excess of the financing rate. For example, a 10-year Japanese government bond has a high carry if the Japanese yield curve is steep. Some macro investors trade on bond carry across countries, buying bonds in countries with high carry while shorting bonds in countries with low carry. Such trades can be implemented with cash bonds (financed in repo), bond futures, or interest-rate swaps. • Yield-curve carry trade: Macro investors also trade bonds of different maturities within the same country. This is called a yield-curve trade. Chapter 14 provides more sophisticated measures of bond carry (that include a so-called roll-down effect) and discusses in more detail how to implement bond and yield-curve trades. • Commodity carry trade: The carry of a commodity futures contract is the amount of money one makes if the spot commodity price does not change.

An example of a classic fixed-income arbitrage trade is to sell short newly issued on-the-run bonds against long positions in older off-the-run bonds. Other classic trades include yield curve trades called butterflies, swap spread trades, mortgage trades, and fixed-income volatility trades. Before we get into the details of these trades, we first consider the fundamentals of bond yields and bond returns. The collection bond yields across all maturities are called the “yield curve” or the “term structure of interest rates.” Fixed-income arbitrage traders are obsessed with the yield curve. We discuss how the yield curve is characterized by its level, slope, and curvature, where the level is set by the central bank, and the slope and curvature are determined by expected future central bank rates and risk premiums.

See also standard deviation (σ) volatility trades, fixed-income, 241, 262 Volcker, Paul, 206 Volcker Rule, 314 volume-weighted average price (VWAP), 67, 68–69 Waddell & Reed Financial, Inc., 155, 156f warrant, 269 Weil, Jonathan, 124 Whitehead, John, 313 Winton Capital Management, 225 Wood Mackenzie, 225 yield curve, 242–43, 243f; bond returns and, 245f (see also bond returns); hedging the risk of parallel moves in, 246; in overheated economy, 191; preferred habitat theory of, 249; Scholes on segmented clienteles concerned with, 263; speculating on the slope of, 190. See also bond yields; term structure of interest rates yield-curve carry trade, 187 yield curve trading, 13, 241, 264–65 yield to maturity (YTM), 179–80, 242, 243f; of corporate bond, 260; determinants of, 248–49; of swap, 259. See also yield curve zero-coupon bond yields, 242–43, 244, 247


pages: 313 words: 101,403

My Life as a Quant: Reflections on Physics and Finance by Emanuel Derman

Berlin Wall, bioinformatics, Black-Scholes formula, Brownian motion, buy and hold, capital asset pricing model, Claude Shannon: information theory, Donald Knuth, Emanuel Derman, fixed income, Gödel, Escher, Bach, haute couture, hiring and firing, implied volatility, interest rate derivative, Jeff Bezos, John Meriwether, John von Neumann, law of one price, linked data, Long Term Capital Management, moral hazard, Murray Gell-Mann, Myron Scholes, Paul Samuelson, pre–internet, publish or perish, quantitative trading / quantitative finance, Sharpe ratio, statistical arbitrage, statistical model, Stephen Hawking, Steve Jobs, stochastic volatility, technology bubble, the new new thing, transaction costs, volatility smile, Y2K, yield curve, zero-coupon bond, zero-sum game

The traders were aware of their need for a better model, and as such were at the forefront of the impetus to replace it. We knew that we had to model the future behavior of all Treasury bonds, that is, the evolution of the entire yield curve. How to set about it was neither obvious nor easy. A stock price is a single number, and when you model its evolution, you project only one number into an uncertain future. In contrast, the yield curve is a continuum, a string or rubber band whose every point, at any instant, represents the yield of a bond with corresponding maturity. As time passes and bond prices change, the yield curve moves, as illustrated in Figure 10.3. To evolve the entire yield curve forward in time is a much more difficult task: Just as you cannot move the different points on a string completely independently of each other, because the string must stay connected, so bonds close to each other must stay connected, too.

We were building a model for traders, and we wanted it to be simple, consistent, and reasonably realistic. Simple meant that only one random factor drove all changes. Consistent meant that it had to value all bonds in agreement with their current market prices; if it produced the wrong bond prices, it was pointless to use it to value options on those bonds. Finally, realistic meant that the model's future yield curves should move through ranges similar to those experienced by actual yield curves. Figure 10.3 Yield curves can vary during the day. When physicists build models, they often first resort to a toy representation of the world in which space and time are discrete and exist only at points on a lattice-it makes picturing the mathematics much easier. We built our model in the same vein. We imagined a world in which the shortest investment you could make lasted exactly one year, and was represented by the one-year Treasury bill interest rate.

But, since the value of the current three-year yield is known, you can use it to deduce the distribution of one-year rates two years hence. Continuing in this way, you can use the current yield curve at any instant to pin down the range of all future one-year rates, as illustrated in Figure 10.5. This was the essence of our model. When Bill and I programmed it, it seemed to work-we could extract the market's expectation of the distributions of future one-year rates from the current yield curve and its volatility. There was nothing holy about the one-year time steps we started with. Once the model worked, we used monthly, weekly, or sometimes even daily steps on a lattice, determining the market's view of the distribution of future short-term rates at any instant from the current yield curve. A typical lattice (or tree, as we called it, because of the way an initial interest rate forked out into progressively wider branches) had hundreds or thousands of equally spaced short periods, as illustrated in Figure 10.6.


pages: 345 words: 86,394

Frequently Asked Questions in Quantitative Finance by Paul Wilmott

Albert Einstein, asset allocation, beat the dealer, Black-Scholes formula, Brownian motion, butterfly effect, buy and hold, capital asset pricing model, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discrete time, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, fudge factor, implied volatility, incomplete markets, interest rate derivative, interest rate swap, iterative process, lateral thinking, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, market bubble, martingale, Myron Scholes, Norbert Wiener, Paul Samuelson, quantitative trading / quantitative finance, random walk, regulatory arbitrage, risk/return, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, stochastic volatility, transaction costs, urban planning, value at risk, volatility arbitrage, volatility smile, Wiener process, yield curve, zero-coupon bond

If the model couldn’t even get bond prices right, how could it hope to correctly value bond options? Thomas Ho and Sang-Bin Lee found a way around this, introducing the idea of yield curve fitting or calibration. See Ho and Lee (1986). 1992 Heath, Jarrow and Morton Although Ho and Lee showed how to match theoretical and market prices for simple bonds, the methodology was rather cumbersome and not easily generalized. David Heath, Robert Jarrow and Andrew Morton took a different approach. Instead of modelling just a short rate and deducing the whole yield curve, they modelled the random evolution of the whole yield curve. The initial yield curve, and hence the value of simple interest rate instruments, was an input to the model. The model cannot easily be expressed in differential equation terms and so relies on either Monte Carlo simulation or tree building.

First, the spot rate does not exist, it has to be approximated in some way. Second, with only one source of randomness the yield curve is very constrained in how it can evolve, essentially parallel shifts. Third, the yield curve that is output by the model will not match the market yield curve. To some extent the market thinks of each maturity as being semi independent from the others, so a model should match all maturities otherwise there will be arbitrage opportunities. Models were then designed to get around the second and third of these problems. A second random factor was introduced, sometimes representing the long-term interest rate (Brennan & Schwartz), and sometimes the volatility of the spot rate (Fong & Vasicek). This allowed for a richer structure for yield curves. And an arbitrary time-dependent parameter (or sometimes two or three such) was allowed in place of what had hitherto been constant(s).

The time-dependent parameter a(t) is chosen so that the theoretical yield curve matches the market yield curve initially. This is calibration. Hull and White There are Hull and White versions of the above models. They take the formsdr = (a(t) − b(t)r)dt + c(t)dX, ordr = (a(t) − b(t)r)dt + c(t)r1/2dX . The functions of time allow various market data to be matched or calibrated. There are solutions for bonds of the form exp(A(t; T) − B(t; T)r). Black-Karasinski In this model the risk-neutral spot-rate process isd(ln r) = (a(t) − b(t) ln rdt + c(t)dX. There are no closed-form solutions for simple bonds. Two-factor models In the two-factor models there are two sources of randomness, allowing a much richer structure of theoretical yield curves than can be achieved by single-factor models.


pages: 353 words: 88,376

The Investopedia Guide to Wall Speak: The Terms You Need to Know to Talk Like Cramer, Think Like Soros, and Buy Like Buffett by Jack (edited By) Guinan

Albert Einstein, asset allocation, asset-backed security, Brownian motion, business cycle, business process, buy and hold, capital asset pricing model, clean water, collateralized debt obligation, computerized markets, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, discounted cash flows, diversification, diversified portfolio, dividend-yielding stocks, dogs of the Dow, equity premium, fixed income, implied volatility, index fund, intangible asset, interest rate swap, inventory management, London Interbank Offered Rate, margin call, money market fund, mortgage debt, Myron Scholes, passive investing, performance metric, risk tolerance, risk-adjusted returns, risk/return, shareholder value, Sharpe ratio, short selling, statistical model, time value of money, transaction costs, yield curve, zero-coupon bond

Related Terms: • Accrual Accounting • Cost of Goods Sold—COGS • Inventory • Asset Turnover • Gross Profit Margin Inverted Yield Curve Yield What Does Inverted Yield Curve Mean? An interest rate environment in which long-term debt instruments have lower yields than do short-term debt instruments of the same credit quality. This type of yield curve is the rarest of the three main curve types and is considered a predictor of economic recession. Maturity Copyright © 2006 Investopedia.com Partial inversion occurs when only some of the short-term Treasuries (5 or 10 years) have higher yields than the 30-year Treasuries; an inverted yield curve sometimes is referred to as a negative yield curve. Investopedia explains Inverted Yield Curve Historically, inversions of the yield curve have preceded many U.S. recessions. Because of this historical correlation, the yield curve often is seen as an accurate indicator of the turning points of the business cycle.

An SEC yield is the percentage yield on a mutual fund based on a 30-day period. 323 324 The Investopedia Guide to Wall Speak Related Terms: • Annual Percentage Yield—APY • Dividend Yield • Yield to Maturity—YTM • Current Yield • Yield Curve Yield Curve Yield What Does Yield Curve Mean? The line on a chart that plots the interest rates, at a set point in time, of bonds that have equal credit quality but different maturity dates. The most frequently reported yield curve compares 3-month, 2-year, 5-year, and 30-year U.S. Treasury debt. This yield curve is used as a benchmark for other debt in the market, such as mortgage rates and bank lending rates. The curve also can be used to predict changes in economic output and growth. Maturity Copyright © 2006 Investopedia.com Investopedia explains Yield Curve The shape of the yield curve is scrutinized closely because it can indicate future changes in interest rates and economic activity. There are three main types of yield curve shapes: (1) normal, (2) inverted, and (3) flat (or humped). (1) A normal yield curve (pictured here) is one in which longer-maturity bonds have a higher yield than do shorter-term bonds because of the risks associated with time. (2) An inverted yield curve is one in which the shorter-term yields The Investopedia Guide to Wall Speak 325 are higher than the longer-term yields; this can be a sign of an upcoming recession. (3) A flat (or humped) yield curve is one in which the shorter-term and longer-term yields are very close to each other; this is also a predictor of an economic transition.

There are three main types of yield curve shapes: (1) normal, (2) inverted, and (3) flat (or humped). (1) A normal yield curve (pictured here) is one in which longer-maturity bonds have a higher yield than do shorter-term bonds because of the risks associated with time. (2) An inverted yield curve is one in which the shorter-term yields The Investopedia Guide to Wall Speak 325 are higher than the longer-term yields; this can be a sign of an upcoming recession. (3) A flat (or humped) yield curve is one in which the shorter-term and longer-term yields are very close to each other; this is also a predictor of an economic transition. The slope of the yield curve also is considered important: the greater the slope, the greater the gap between short-term and long-term rates. Related Terms: • Corporate Bond • Inverted Yield Curve • U.S. Treasury • Interest Rate • Risk-Free Rate of Return Yield to Maturity (YTM) What Does Yield to Maturity Mean?


pages: 289 words: 113,211

A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation by Richard Bookstaber

"Robert Solow", affirmative action, Albert Einstein, asset allocation, backtesting, beat the dealer, Black Swan, Black-Scholes formula, Bonfire of the Vanities, butterfly effect, commoditize, commodity trading advisor, computer age, computerized trading, disintermediation, diversification, double entry bookkeeping, Edward Lorenz: Chaos theory, Edward Thorp, family office, financial innovation, fixed income, frictionless, frictionless market, George Akerlof, implied volatility, index arbitrage, intangible asset, Jeff Bezos, John Meriwether, London Interbank Offered Rate, Long Term Capital Management, loose coupling, margin call, market bubble, market design, merger arbitrage, Mexican peso crisis / tequila crisis, moral hazard, Myron Scholes, new economy, Nick Leeson, oil shock, Paul Samuelson, Pierre-Simon Laplace, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk tolerance, risk/return, Robert Shiller, Robert Shiller, rolodex, Saturday Night Live, selection bias, shareholder value, short selling, Silicon Valley, statistical arbitrage, The Market for Lemons, time value of money, too big to fail, transaction costs, tulip mania, uranium enrichment, William Langewiesche, yield curve, zero-coupon bond, zero-sum game

Plotting these payouts forms the well-known yield curve. Although the prices of the bonds and their respective yields vary as circumstances change—inflation, recession, war—each interest rate along the yield curve is flexibly but securely tethered to its neighboring rates in a way that can be described mathematically. CRUNCH TIME AT MORGAN STANLEY The job of the quants descending on Wall Street was to exploit the relationships along the yield curve, to develop mathematical models that would tease a higher return out of a bond portfolio or a bond trading operation than the green-eyeshade gang could. By the early 1980s, a number of other firms were already riding the number crunching wave. Marty Leibowitz at Salomon had built a strong team that was at the top of the heap for fixed income portfolio strategy and yield-curve trading.

Meanwhile, in Orange County, California, treasurer Robert Citron had been structuring trades with the help of friends at Merrill Lynch to borrow on the short end of the yield curve to finance positions in the usually higher-yielding intermediate-term rates. Citron’s strategy depended on short-term interest rates remaining relatively low when compared with medium-term interest rates. This they did in the early 1990s, so Citron’s yield curve bet made money and everyone was happy, with no questions asked. Even in early 1994, when his strategy began to go south, he survived an election that focused attention on his financial management, convincing voters that the criticisms were just so much politically motivated rhetoric. Then the Fed started raising rates in February 1994, and the yield curve started to move the wrong way. Orange County got crushed. A succession of hikes saddled Citron’s fund with losses of approximately $1.7 billion, around 20 percent of its value.

The trade might have just happened to be getting cheaper at the same time interest rates were dropping. Markets either go up, go down, or stay the same, so if you are losing money in one there are bound to be others that will be losing at the same time. That does not mean the two are functionally linked. Another possibility was that the arb model did not pick up all of the factors affecting interest rates. The model was a proprietary yield curve model dubbed the “two plus” because it looked at the yield curve as two factors, plus a parameter to signal the effects of Federal Reserve policy shifts. The two-plus model was the citadel of intellectual capital for the group. It was a closely guarded secret, although, despite the group’s best efforts, it found its way to a number of other firms as talent was periodically bid away. 85 ccc_demon_077-096_ch05.qxd 2/13/07 A DEMON 1:45 PM OF Page 86 OUR OWN DESIGN The model was developed by Bill Krasker in the mid-1980s shortly after he came to Salomon from a brief stint teaching at Harvard.


pages: 348 words: 99,383

The Financial Crisis and the Free Market Cure: Why Pure Capitalism Is the World Economy's Only Hope by John A. Allison

Affordable Care Act / Obamacare, American ideology, bank run, banking crisis, Bernie Madoff, business cycle, clean water, collateralized debt obligation, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, disintermediation, fiat currency, financial innovation, Fractional reserve banking, full employment, high net worth, housing crisis, invisible hand, life extension, low skilled workers, market bubble, market clearing, minimum wage unemployment, money market fund, moral hazard, negative equity, obamacare, Paul Samuelson, price mechanism, price stability, profit maximization, quantitative easing, race to the bottom, reserve currency, risk/return, Robert Shiller, Robert Shiller, The Bell Curve by Richard Herrnstein and Charles Murray, too big to fail, transaction costs, yield curve, zero-sum game

What is sad, however, is that even though at some level Bernanke knew that the Fed had made major mistakes, this is not what he discussed publicly. He said repeatedly that the inverted yield curve would not cause a recession, but would simply slow the rate of inflation. While he mentioned the housing market occasionally, mostly by claiming that there was no bubble,15 his focus was primarily on commodity prices. He held the inverted yield curve for more than a year (from July 2006 to January 2008), one of the longest yield-curve inversions ever. The subsequent Great Recession, which lasted through June 2009 (and, practically speaking, continues in December 2011), began in December 2007. As mentioned, history reveals that there is a very high correlation between inverted yield curves and recessions. Bernanke denied this correlation and was adamant that things were different this time because of globalization.

The inflation rate using the “old” CPI is significantly higher than that using Greenspan’s calculation.13 Could the Fed be making improper decisions based on miscalculating the CPI? At best, the calculation of the CPI is more art than science. After he became chairman of the Federal Reserve in early 2006, Bernanke rapidly raised interest rates and created an inverted yield curve. An inverted yield curve is one in which short-term rates are higher than long-term rates, and even Fed researchers acknowledge that an inverted yield curve tends to trigger recessions.14 Because bankers had been misled by Greenspan’s often-spoken concern about deflation, many of them had extended their bond portfolios, as this was one of the few areas where they could make long-term profits based on Greenspan’s deflation scenario. (Greenspan based his assumptions on his belief in “excess” global savings driven by the Chinese.)

The rapid increase in interest rates was far more destructive than the level of rates. Also, the unanticipated pace and magnitude of rising interest rates left bankers in a very difficult position. Inversions of yield curves are an unusual phenomenon. Typically, investors will invest for a longer duration only if they can earn a higher interest rate, because, other things being equal, the longer the investment, the greater the risk and the lower the liquidity. Markets practically never invert yield curves. It is interesting that Bernanke’s decision to both raise short-term interest rates (to a peak of 5.25 percent) and invert the yield curve must have reflected his realization that Greenspan’s policies had been inflating the economy and leading to misinvestment (overinvestment in housing). Greenspan himself seemed to have realized it, since as chairman he had raised the fed funds rate from 1 percent in 2004 to 4.5 percent before leaving office in early 2006.


pages: 385 words: 128,358

Inside the House of Money: Top Hedge Fund Traders on Profiting in a Global Market by Steven Drobny

Albert Einstein, asset allocation, Berlin Wall, Bonfire of the Vanities, Bretton Woods, business cycle, buy and hold, buy low sell high, capital controls, central bank independence, commoditize, commodity trading advisor, corporate governance, correlation coefficient, Credit Default Swap, diversification, diversified portfolio, family office, fixed income, glass ceiling, high batting average, implied volatility, index fund, inflation targeting, interest rate derivative, inventory management, John Meriwether, Long Term Capital Management, margin call, market bubble, Maui Hawaii, Mexican peso crisis / tequila crisis, moral hazard, Myron Scholes, new economy, Nick Leeson, oil shale / tar sands, oil shock, out of africa, paper trading, Paul Samuelson, Peter Thiel, price anchoring, purchasing power parity, reserve currency, risk tolerance, risk-adjusted returns, risk/return, rolodex, Sharpe ratio, short selling, Silicon Valley, The Wisdom of Crowds, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond, zero-sum game

We had other trades on that were doing well. We also hung on to that trade for so long because it was so outstandingly good. I have never seen a yield curve that was as mispriced as the yen curve at that time. Other great trades over the years were curvature and conditional steepener type trades on the U.S. yield curve back in 2001 when nobody understood them. Now everyone understands them so there is not much juice left in it. The trade is where you buy a receiver swaption or a call on the oneyear interest rate one-year forward, and sell the same on the 10-year interest rate.There is a slight macro bias to this trade as it is a pure curve trade, which I consider more macro than RV. We built models of the whole yield curve out to 30 years to see what we thought the shape of the curve would be at any given rate level, how convex we thought the curve should be, why it should be that convex, and so on.

When you understand the process, then the whole out-of-control process is oddly very much in control. Because as a control freak, you’re always looking out ahead of you, you’re looking out ahead for trouble, and that’s exactly what you have to do in markets, is try to look forward. So much of market talk, market analysis, and trading is based on what’s happened in the past.“The yield curve is flat, therefore it tells you it’s a recession,” or “The yield curve is steep, which tells you there’s going to be a recovery.”Those types of historical examples are of very little value, so a control freak is always trying to look forward and trying to look ahead. What do you think differentiates a good analyst and a good trader? The good trader knows how to actively manage the risk and run a position. A good analyst should help find the trade or look for pitfalls in the trade.

Our risk is not limited to Barclays’ outstanding liabilities.We are actively managing risk and seeking a positive absolute return while being limited by the firm’s value at risk (VAR) model, regulatory capital limits, and balance sheet limits. We look to maximize current income for a given unit of risk. As a result, we tend to be in the front end of the yield curve as opposed to the back end because it’s better to roll one billion one-year notes for 10 years than to buy 100 million 10-year bonds ceteris paribus.The VAR would be the same if they had the same volatility but with the one-year notes, you get much more current income. By concentrating risk in the front end of the yield curve, the only thing that can really make me right or wrong is a central bank. A central bank has the ability to enhance or diminish my carry, and we want carry. Everything else is just noise. Our area of core expertise is the one-year, one-year interest rate forwards.


pages: 349 words: 134,041

Traders, Guns & Money: Knowns and Unknowns in the Dazzling World of Derivatives by Satyajit Das

accounting loophole / creative accounting, Albert Einstein, Asian financial crisis, asset-backed security, beat the dealer, Black Swan, Black-Scholes formula, Bretton Woods, BRICs, Brownian motion, business process, buy and hold, buy low sell high, call centre, capital asset pricing model, collateralized debt obligation, commoditize, complexity theory, computerized trading, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, currency peg, disintermediation, diversification, diversified portfolio, Edward Thorp, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, financial innovation, fixed income, Haight Ashbury, high net worth, implied volatility, index arbitrage, index card, index fund, interest rate derivative, interest rate swap, Isaac Newton, job satisfaction, John Meriwether, locking in a profit, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, Marshall McLuhan, mass affluent, mega-rich, merger arbitrage, Mexican peso crisis / tequila crisis, money market fund, moral hazard, mutually assured destruction, Myron Scholes, new economy, New Journalism, Nick Leeson, offshore financial centre, oil shock, Parkinson's law, placebo effect, Ponzi scheme, purchasing power parity, quantitative trading / quantitative finance, random walk, regulatory arbitrage, Right to Buy, risk-adjusted returns, risk/return, Satyajit Das, shareholder value, short selling, South Sea Bubble, statistical model, technology bubble, the medium is the message, the new new thing, time value of money, too big to fail, transaction costs, value at risk, Vanguard fund, volatility smile, yield curve, Yogi Berra, zero-coupon bond

P & G lost (from increases in the spread) if rates increased at either point in the yield curve. P & G had, knowingly or unknowingly, sold six month put options on the five year CMT rates and on the 30 year Treasury bond price. The premium received lowered the cost of borrowing. The structure had greater exposure to the five year CMT rate. The five year CMT rate was multiplied by 17.04 (in the simplified version of the formula) to convert the five year CMT rate into a price. After adjustment for this, the five year CMT was around four times the 30 year position in terms of amount and around two times in price sensitivity terms. Nero would have instantly recognized the leverage in the structure. The trade was very sensitive to interest rates. If rates increased across the yield curve, then the spread increased. If the yield curve flattened (the difference in rates between the five year CMT and the 30 year Treasury bond decreased), then the spread increased.

It was invented by traders in agricultural futures and is generally attributed to Holbrook Working. Swaps? They are simply a collection of forwards. There are subtle problems. Is the income on the asset a known known? In the case of shares, dividends are a known unknown and tend to cause heartache. Also we need a yield curve; that is, the interest rates for borrowing and lending for different dates. Interest rates are frequently not available for every maturity. Quants have elaborate models for creating ‘complete’ and ‘parsimonious’ yield curves. I have no idea why the yield curve has to be stingy, that is, parsimonious. The rocket scientists emphasize that this is important, citing Occam’s razor. Occam was a fourteenth century logician and Franciscan friar in the English county of Surrey. The principle states that: ‘Entities should not be multiplied unnecessarily.’

The arrangement was that OCM paid dollar LIBOR minus 40 basis points (that is, 0.40% pa),’ I explained. ‘Yes, yes. Cheaper cost. We get cheaper funding. Save 40 basis points. Cheap money.’ Budi’s excitement was palpable. ‘Bank advise us,’ Adewiko added quickly. ‘Bank give us detail presentation. They say dollar yield curve very steep. Get value from steep curve using arrears swap.’ Adewiko displayed surprising animation. ‘Bank know Greenspan. Play tennis with him.’ I must have looked surprised. ‘Bank advise us,’ Adewiko said gloomily, remembering the script. I referred to my notes. ‘Then, you terminated the arrears swap.’ ‘Take profit, take profit,’ Budi interrupted. ‘Dollar yield curve flatten. We take profit.’ DAS_C01.QXD 5/3/07 11:45 PM Page 7 P ro l o g u e 7 I could imagine what had happened. The dealers had played these guys for the I could imagine what had suckers they were. OCM had entered into happened.


The Concepts and Practice of Mathematical Finance by Mark S. Joshi

Black-Scholes formula, Brownian motion, correlation coefficient, Credit Default Swap, delta neutral, discrete time, Emanuel Derman, fixed income, implied volatility, incomplete markets, interest rate derivative, interest rate swap, London Interbank Offered Rate, martingale, millennium bug, quantitative trading / quantitative finance, short selling, stochastic process, stochastic volatility, the market place, time value of money, transaction costs, value at risk, volatility smile, yield curve, zero-coupon bond

Develop an analytic formula for its price if the forward rate follows geometric Brownian motion. 14 The pricing of exotic interest rate derivatives 14.1 Introduction The critical difference between modelling interest rate derivatives and equity/FX options is that an interest rate derivative is really a derivative of the yield curve and the yield curve is a one-dimensional object whereas the price of a stock or an FX rate is zero-dimensional. One might be tempted to think that as most movements of the yield curve are up and down it is unnecessary to model the one-dimensional behaviour. However, the yield curve can and does change shape over time, and we shall see that for certain options these changes are the source of most of the option's value. From time to time, yield curves also undergo qualitative changes in shape. For example, the UK yield curve changed from being upward-sloping to being humped in the early 1990s. The fact that we are modelling the changes of a curve makes life considerably more complicated but also much more interesting.

This is a distortion for a number of reasons but is nevertheless a reasonable way to proceed: in this section we explain why. There are, in fact, many yield curves for each currency whose levels depend on the riskiness of the instruments involved. We discuss the curves for sterling but the issues are essentially the same for the euro and US dollar curves. We will generally talk about constructing discount curves rather than yield curves, as the discount curve is just the price of a zero-coupon bond as a function of maturity which is what we generally want. On the other hand, the yield curve is a notional measure of the effective annual interest rate which we would receive for investing in such a bond. The yield curve is useful from a qualitative point of view as it strips out redundant information by converting everything to interest rates, but to work mathematically with the yield curve is simply annoying. With all the discount curves, one thing to bear in mind is that the theoretical curve will not actually represent a price one can obtain in the market.

Pricing a reversing pair is trivial: we just decompose it into a sum of two forward-rate agreements, which we already know how to price, and we are done. The interesting thing about the reversing pair is that its value is very insensitive to changes in the overall level of the yield curve. If interest rates go up by 1%, then we gain on the first forward-rate agreement but lose a similar amount on the 319 320 The pricing of exotic interest rate derivatives second. If, however, the shape of the yield curve changes so that the first rate goes down and the second rate goes up, then we lose on the first and lose on the second. Thus the value of the reverse contract reflects changes in the shape but not the level of the yield curve. In particular, the reverse contract is sensitive to the slope of the curve: a change in slope means money won or lost. We can extend the reverse contract to a double reverse contract by taking two reverse contracts over adjacent periods of time which go in opposite directions.


pages: 333 words: 76,990

The Long Good Buy: Analysing Cycles in Markets by Peter Oppenheimer

"Robert Solow", asset allocation, banking crisis, banks create money, barriers to entry, Berlin Wall, Big bang: deregulation of the City of London, Bretton Woods, business cycle, buy and hold, Cass Sunstein, central bank independence, collective bargaining, computer age, credit crunch, debt deflation, decarbonisation, diversification, dividend-yielding stocks, equity premium, Fall of the Berlin Wall, financial innovation, fixed income, Flash crash, forward guidance, Francis Fukuyama: the end of history, George Akerlof, housing crisis, index fund, invention of the printing press, Isaac Newton, James Watt: steam engine, joint-stock company, Joseph Schumpeter, Kickstarter, liberal capitalism, light touch regulation, liquidity trap, Live Aid, market bubble, Mikhail Gorbachev, mortgage debt, negative equity, Network effects, new economy, Nikolai Kondratiev, Nixon shock, oil shock, open economy, price stability, private sector deleveraging, Productivity paradox, quantitative easing, railway mania, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, secular stagnation, Simon Kuznets, South Sea Bubble, special economic zone, stocks for the long run, technology bubble, The Great Moderation, too big to fail, total factor productivity, trade route, tulip mania, yield curve

In the absence of inflation pressures, monetary policy may remain much looser and reduce the risks of recession and, by association, bear markets. The yield curve. Related to the point about inflation, tighter monetary policy often leads to a flattening, or even inverted, yield curve. Because many, although by no means all, bear markets are preceded by periods of monetary policy tightening, we find that flat yield curves, prior to inversion, are also followed by low returns or bear markets. In recent years the impact of QE and falling inflation expectations (term premia), may have weakened the reliability of this signal.4 As a consequence, we use the 3-month to 10-year measure, with a focus on the short end of the yield curve (0–6 quarter). The 0–6 quarter forward spread more clearly captures the market's near-term outlook via its funds rate expectations than back-end measures, which are more distorted by term premia.

But, over short-term periods, the moves in bond yields – and indeed the general shape of the yield curve (whether bond yields are higher or lower than short-term interest rates) – matters a great deal. Depending on what is happening to inflation expectations, it is possible that improving growth prospects coupled with rising rates are associated with the strongest returns in equity markets. This is particularly true – as in the recent cycle – when the starting level of interest rates is very low as rising bond yields, alongside growth expectations, may reflect more confidence that policy is working and that recessionary risks are fading. By the same token, a steepening yield curve (long-term bond yields rising above the levels of short-term interest rates) would generally imply a supportive central bank monetary policy, and an inverted yield curve, when bond yields are below short-term, policy-driven interest rates, would tend to reflect a restrictive monetary stance.

Asset prices, financial and monetary stability: Exploring the nexus. BIS Working Papers No 114 [online]. Available at https://www.bis.org/publ/work114.html 3 Oppenheimer, P., and Bell, S. (2017). Bear necessities: Identifying signals for the next bear market. London, UK: Goldman Sachs Global Investment Research. 4 A useful discussion about the value of the yield curve in predicting recessions can be found in Benzoni, L., Chyruk, O., and Kelley, D. (2018). Why does the yield-curve slope predict recessions? Chicago Fed Letter No. 404. 5 A discussion of a broad recession risk indicator and the private sector imbalance can be found in Struyven, D., Choi, D., and Hatzius, J. (2019). Recession risk: Still moderate. New York, NY: Goldman Sachs Global Investment Research. Chapter 7 Bull's Eye: The Nature and Shape of Bull Markets Bull markets, like bear markets, can be defined in many different ways.


pages: 253 words: 79,214

The Money Machine: How the City Works by Philip Coggan

activist fund / activist shareholder / activist investor, algorithmic trading, asset-backed security, Bernie Madoff, Big bang: deregulation of the City of London, bonus culture, Bretton Woods, call centre, capital controls, carried interest, central bank independence, collateralized debt obligation, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, disintermediation, diversification, diversified portfolio, Edward Lloyd's coffeehouse, endowment effect, financial deregulation, financial independence, floating exchange rates, Hyman Minsky, index fund, intangible asset, interest rate swap, Isaac Newton, joint-stock company, labour market flexibility, large denomination, London Interbank Offered Rate, Long Term Capital Management, merger arbitrage, money market fund, moral hazard, mortgage debt, negative equity, Nick Leeson, Northern Rock, pattern recognition, purchasing power parity, quantitative easing, reserve currency, Right to Buy, Ronald Reagan, shareholder value, South Sea Bubble, sovereign wealth fund, technology bubble, time value of money, too big to fail, tulip mania, Washington Consensus, yield curve, zero-coupon bond

Logical though the above arguments are, it often happens that long-term interest rates are below short-term rates. To understand why, we must look at the yield curve. The Yield Curve We have already proposed a general principle of finance – that lesser liquidity demands greater reward. That being the case, longer-term instruments should always bear a higher interest rate than short-term ones. This is not always true. Long-term rates can be the same as, or lower than, those of short-term instruments. A curve can be drawn which links the different levels of rates with the different maturities of debt. If long-term rates are above short-term ones, this is described as a positive or upward-sloping yield curve. If short-term rates are higher, the curve is described as negative or inverted. What determines the shape of the yield curve? The three main theories used to explain its structure are the liquidity theory, the expectations theory and the market-segmentation theory.

Borrowers (in particular, businesses) will be prepared to pay higher interest rates in order to secure long-term funds for investment. Thus, other things being equal, the yield curve will be upward-sloping. The expectations theory holds that the yield curve represents investors’ views on the likely future movement of short-term interest rates. If one-year interest rates are 10 per cent and an investor expects them to rise to 12 per cent in a year’s time, he will be unwilling to accept 10 per cent on a two-year loan. It would be more profitable for him to lend for one year and then re-lend his money at the higher rate. A two-year loan will therefore have to offer at least 11 per cent a year before the investor will be attracted. Thus if interest rates are expected to rise, the yield curve will be upward-sloping. If investors expect short-term interest rates to fall, however, they will seek to lend long-term.

Thus a bond investor who expected rates to rise will sell his bonds before the rise in rates and the resultant fall in the bond price occurs. The investor will hold the funds in the most liquid form available so that he can reinvest them as soon as rates rise. If the same investor expects interest rates to fall, he will hold on to the bonds because their price will rise as rates fall. The third theory of the yield curve is the market-segmentation theory. This assumes that the markets for the different maturities of debt instruments are entirely separate. Within each segment interest rates are set by supply and demand. The shape of the yield curve will be determined by the different results of supply/demand trade-offs. If a lot of borrowers have long-term financing needs and few investors want to lend for such periods, the curve will be upward-sloping. If borrowers demand short-term funds and investors prefer to lend for longer periods, the curve will be downward-sloping.


pages: 701 words: 199,010

The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini

affirmative action, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bernie Madoff, Black-Scholes formula, business cycle, buttonwood tree, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, John Meriwether, Kickstarter, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low skilled workers, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, Mitch Kapor, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, Northern Rock, Occupy movement, oil shock, price stability, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sam Peltzman, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, survivorship bias, systematic trading, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

Box 2.2 Salomon Arb Group Interview Question Question: Your portfolio group strongly believes that the yield curve is going to flatten very soon. It could be that short-term rates will rise or long-term rates will fall or some combination of the two. Suppose also that you have three instruments available: a 30-year zero-coupon bond, a 1-year Treasury bill, and a cash account. Suppose the modified duration of the 30-year is 28 and the modified duration of the 1-year is 1. What strategy should you pursue to benefit from your beliefs? Suggested Solution: The investor would ideally like to have no interest-rate exposure, but take a view on the flattening yield curve. Thus, one would like to hedge parallel yield curve shifts, but take advantage of the nonparallel moves. One way to do this would be to buy long bonds and to short short-term notes (e.g., buy the 30-year Treasury and short the 1-year note).

LTCM also purchased AAA-rated tranches of structured products backed by commercial mortgages and paid fixed rates on swaps, taking advantage of the yield spread. This is also a mortgage trade, one that caused a lot of trouble for hedge funds in 2008. LTCM’s fixed-income portfolio included butterfly yield curve trades. A butterfly trade is typically one in which a trader is long the 30-year bond and the 5-year bond, but short the 10-year bond. The trade is neutral to general interest rate movements, but takes a view that the yield curve will become more hump shaped. A trader could take the other view by changing the long positions to short positions. Either view could apply to any part of the yield curve, not just the 5-10-30 combo. In 1998, LTCM had a relative butterfly trade on in Germany and the UK. LTCM executed this trade in the swap market. In the UK, it paid fixed interest on the 20-year and 3-year area of the curve, and received fixed in the 7-year area.

PGAM, JWMP, and other funds had this trade on in 2008. They could implement it with government securities or swaps, but typically executed it with swaps. The trade is short the 30-year and 5-year areas of the yield curve and long the 10-year part of the curve. It’s constructed to eliminate interest-rate risk (duration neutral) and eliminate curve-slope risk. This position lost quite a bit in 2008. PGAM (and others) had a large position in this trade across a variety of different currencies. For PGAM, this was a hedge position, designed to diversify its holdings. This trade should have done well in a crisis, when the yield curve typically steepens and the 10- to 30-year part of the curve steepens more than the 5- to 10-year area. When monetary authorities lower interest rates, natural long-term debt buyers such as pension funds shy away from the long end of the curve, and higher risk aversion means that investors shorten durations, moving away from longer-dated securities.


pages: 397 words: 112,034

What's Next?: Unconventional Wisdom on the Future of the World Economy by David Hale, Lyric Hughes Hale

affirmative action, Asian financial crisis, asset-backed security, bank run, banking crisis, Basel III, Berlin Wall, Black Swan, Bretton Woods, business cycle, capital controls, Cass Sunstein, central bank independence, cognitive bias, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, corporate social responsibility, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, Daniel Kahneman / Amos Tversky, debt deflation, declining real wages, deindustrialization, diversification, energy security, Erik Brynjolfsson, Fall of the Berlin Wall, financial innovation, floating exchange rates, full employment, Gini coefficient, global reserve currency, global village, high net worth, Home mortgage interest deduction, housing crisis, index fund, inflation targeting, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Just-in-time delivery, Kenneth Rogoff, Long Term Capital Management, Mahatma Gandhi, Martin Wolf, Mexican peso crisis / tequila crisis, Mikhail Gorbachev, money market fund, money: store of value / unit of account / medium of exchange, mortgage tax deduction, Network effects, new economy, Nicholas Carr, oil shale / tar sands, oil shock, open economy, passive investing, payday loans, peak oil, Ponzi scheme, post-oil, price stability, private sector deleveraging, purchasing power parity, quantitative easing, race to the bottom, regulatory arbitrage, rent-seeking, reserve currency, Richard Thaler, risk/return, Robert Shiller, Robert Shiller, Ronald Reagan, sovereign wealth fund, special drawing rights, technology bubble, The Great Moderation, Thomas Kuhn: the structure of scientific revolutions, Tobin tax, too big to fail, total factor productivity, trade liberalization, Washington Consensus, Westphalian system, WikiLeaks, women in the workforce, yield curve

However, the hawkishness of Asia’s central banks also means that while OECD yield curves are steep and likely to remain so for the foreseeable future, Asian yield curves are now flattening rapidly all across the board. One amazing development is that the US (and German) yield curves have, since 2009, continued to shift lower in spite of the economic recovery. In Asia, we are seeing exactly the opposite, with yield curves flattening (in Malaysia, Indonesia, China, Thailand, Australia, etc.) or shifting higher (India), a divergence in trend that can only logically be explained by the differences in monetary policy. And, of course, this should logically have an impact on currency markets since steep yield curves often weaken currencies, while flat or inverted yield curves strengthen them (cash becomes harder to find, thereby inviting companies and individuals to repatriate capital from abroad, etc.).

Indeed, most Asian equity indices are typically comprised of 20–25 percent of exporting stocks (which should struggle as Asian currencies move higher) and 30–35 percent of Asian financials (for whom the flattening yield curves could prove a headwind). In other words, investors into Asia who decide to solely get exposure through benchmark ETFs are likely investing more than half of their money in what should prove to be “dead money.” Asia’s very different cyclical and policy outlook argues against investing in indices and instead for concentrating on the parts of the market that will benefit from the higher currencies and lower long-term interest rates. This of course includes long-dated Asian government bonds, high-dividend yield-paying stocks (which tend to always outperform when yield curves flatten and/or invert), utility stocks, local consumption stocks, and all the “stable growth” stocks, whether pharmaceuticals, consumer staples, software and tech stocks, and so on.

But what is interesting is that since 2009, stocks linked to local consumption, which one would expect to hold up decently as yield curves flatten, have done precisely that. In a sign of unprecedented maturity for the Chinese market, most of the sectors listed above are actually trading at higher levels than they did when the Chinese market peaked in August 2009! Figure 6.2 Outperformers since Market Peaked in August 2009 Source: Thomson Reuters The Second Challenge: A Structural Shift? We would be remiss to mention the outperformers in China’s current bear market without highlighting the sectors that have brought the index down. And here, one finds mostly the sectors one would expect to see penalized by a flatter yield curve, whether financials, steel and cement (since there should be less construction), mining, oil and gas, real estate, and so on.


A Primer for the Mathematics of Financial Engineering by Dan Stefanica

asset allocation, Black-Scholes formula, capital asset pricing model, constrained optimization, delta neutral, discrete time, Emanuel Derman, implied volatility, law of one price, margin call, quantitative trading / quantitative finance, Sharpe ratio, short selling, time value of money, transaction costs, volatility smile, yield curve, zero-coupon bond

BONDS. 72 which is equivalent to flB ~ -fly D. (2.59) B In other words, the percentage change in the price of the bond can be approximated by the duration of the bond multiplied by the parallel shift in the yield curve, with opposite sign. For very small parallel shifts in the yield curve, the approximation formula (2.59) is accurate. For larger parallel shifts, convexity is used to better capture the effect of the changes in the yield curve on the price of the bond. Definition 2.5. The convexity C of a bond with price B and yield y is 1 82 B C=B8 y 2· 2.8. NUMERICAL IMPLEMENTATION OF BOND MATHEMATICS 73 From (2.62) and (2.64), we conclude that y = r(O, T). In other words, the yield of a zero coupon bond is the same as the zero rate corresponding to the maturity of the bond. This explains why the zero rate curve r(O, t) is also called the yield curve. As expected, the duration of a zero coupon bond is equal to the maturity of the bond.

(J" Thus, the implied volatility approximate value is within 0.015% of the volatility used to price the call option, which is remarkably good accuracy. 0 I(J" - (J"imp,approxl CHAPTER 5. TAYLOR'S FORMULA. TAYLOR SERIES. 170 5.6 Connections between duration and convexity Recall from section 2.7 that bond duration measures the change in the price of a bond with respect to changes in the yield curve, while bond convexity measures the change of the duration of a bond with respect to changes in the yield curve, i.e., D= 1 82B C = B 8 y 2' 1 8B and B 8y (5.94) Also, recall that the value B of a bond with yield y paying cash-flows Ci and time t i , i = 1 : n, is B = 2:7=1 Cie-yti. To emphasize that the value of the bond is a function of its yield, we denote B by B (y), i.e., n B(y) = L Cie- yti . (5.95) 5.6. CONNECTIONS BETWEEN DURATION AND CONVEXITY 171 for the function f(x) = B(y), and for the points x = y + ~y and a = y: B(y + ~y) ~ B(y) + ~y B'(y) + (~y)2 BI/(y). (5.97) 2 Let ~B = B(y + ~y) - B(y).

For continuously compounded interest, the value at time t of B(O) currency units (e.g., U.S. dollars) is where exp(x) = eX. The value at time of B(t) currency units at time t is r(t) = lim ~ . B(t + dt) - B(t) = B'(t). dt-70 dt B(t) B(t) ----------------------2We note, and further explain this in section 2.7.1, that r(O,t) is the yield of a zero~ coupon bond with maturity t. The zero rate curve is also called the yield curve. r(T) dT), V t > 0; (2.39) from (2.39) is called the discount factor. r(O, t) = ~ t rt r(T) dT. (2.40) 10 In other words, the zero rate r(O, t) is the average of the instantaneous rate r (t) over the time interval [0, t] . If r( t) is continuous, then it is uniquely determined if the zero rate curve r(O, t) is known. From (2.40), we obtain that 1'r(T) dT = t ·r(O, t). (2.41) By differentiating (2.41) with respect to t, see, e.g., Lemma 1.2, we find that r(t) = r(O, t) (2.36) The instantaneous rate r(t) at time t is the rate of return of deposits made at time t and maturing at time t + dt, where dt is infinitesimally small, i.e., J~ r(T) dT) (-1' From (2.35) and (2.38), it follows that (2.35) ° B(O) = exp( -t r(O, t)) B(t)


pages: 338 words: 104,684

The Deficit Myth: Modern Monetary Theory and the Birth of the People's Economy by Stephanie Kelton

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, Affordable Care Act / Obamacare, American Society of Civil Engineers: Report Card, Asian financial crisis, bank run, Bernie Madoff, Bernie Sanders, blockchain, Bretton Woods, business cycle, capital controls, central bank independence, collective bargaining, COVID-19, Covid-19, currency manipulation / currency intervention, currency peg, David Graeber, David Ricardo: comparative advantage, decarbonisation, deindustrialization, discrete time, Donald Trump, eurozone crisis, fiat currency, floating exchange rates, Food sovereignty, full employment, Gini coefficient, global reserve currency, global supply chain, Hyman Minsky, income inequality, inflation targeting, Intergovernmental Panel on Climate Change (IPCC), investor state dispute settlement, Isaac Newton, Jeff Bezos, liquidity trap, Mahatma Gandhi, manufacturing employment, market bubble, Mason jar, mortgage debt, Naomi Klein, new economy, New Urbanism, Nixon shock, obamacare, open economy, Paul Samuelson, Ponzi scheme, price anchoring, price stability, pushing on a string, quantitative easing, race to the bottom, reserve currency, Richard Florida, Ronald Reagan, shareholder value, Silicon Valley, trade liberalization, urban planning, working-age population, Works Progress Administration, yield curve, zero-sum game

Since 2016, Japan’s central bank has been explicitly targeting its yield curve.31 That means the BOJ isn’t just controlling the overnight interest rate (as the Fed does in the US) but also effectively setting long-term rates as well. The practice is known as yield curve control because it literally involves controlling the yield on ten-year government bonds. Today, the BOJ is committed to holding the ten-year rate at around zero percent. To do that, the central bank simply buys bonds in whatever quantity is necessary to prevent yields from rising above zero. It’s a bit akin to quantitative easing in that lower interest rates are the objective. However, yield curve control is a stronger form of commitment since the quantity of bonds the BOJ will buy in any given time period is not determined ahead of time. Yield curve control is about committing to an interest rate (price) target rather than committing to purchase a certain amount (quantity) of bonds.

Even after the war ended, the Fed continued to anchor the long-term interest rate on behalf of the government. Coordination with fiscal policy officially ended in 1951, with an agreement known as the Treasury–Federal Reserve Accord, which freed the Fed to pursue independent monetary policy.31 Elsewhere, central banks are returning to explicit coordination of fiscal and monetary policy.32 For more than three years, the BOJ has been engaged in a policy known as yield curve control. In addition to anchoring the short-term interest rate, the BOJ committed to pinning rates on ten-year government bonds (known as Japanese Government Bonds or JGBs) near zero. In carrying out that policy, the BOJ has purchased massive amounts of government debt, buying up ¥6.9 trillion in June 2019 alone.33 As a result of its aggressive bond-buying program, the BOJ now holds roughly 50 percent of all Japanese government bonds.

(In the futures market, traders bet directly on how the Fed will adjust the federal funds rate, and their winnings or losses are directly tied to whether they guess right or wrong. Statistically, the federal funds futures market is where the most accurate predictions are made.) This gives central banks reasonably strong influence over long-term rates. To exercise even stronger control, central banks can effectively set rates across the yield curve, as Japan has done. 8. The term bond vigilantes refers to the power of financial markets (or, more accurately, investors in financial markets) to force sharp movements in the price of a financial asset like government bonds so that the interest rate swings unexpectedly. Ultimately, the European Central Bank did keep the vigilantes at bay, but not without imposing painful austerity on the Greek people.


pages: 320 words: 33,385

Market Risk Analysis, Quantitative Methods in Finance by Carol Alexander

asset allocation, backtesting, barriers to entry, Brownian motion, capital asset pricing model, constrained optimization, credit crunch, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, en.wikipedia.org, fixed income, implied volatility, interest rate swap, market friction, market microstructure, p-value, performance metric, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, stochastic volatility, Thomas Bayes, transaction costs, value at risk, volatility smile, Wiener process, yield curve, zero-sum game

To Walter Ledermann Contents List of Figures xiii List of Tables xvi List of Examples xvii Foreword xix Preface to Volume I I.1 Basic Calculus for Finance I.1.1 Introduction I.1.2 Functions and Graphs, Equations and Roots I.1.2.1 Linear and Quadratic Functions I.1.2.2 Continuous and Differentiable Real-Valued Functions I.1.2.3 Inverse Functions I.1.2.4 The Exponential Function I.1.2.5 The Natural Logarithm I.1.3 Differentiation and Integration I.1.3.1 Definitions I.1.3.2 Rules for Differentiation I.1.3.3 Monotonic, Concave and Convex Functions I.1.3.4 Stationary Points and Optimization I.1.3.5 Integration I.1.4 Analysis of Financial Returns I.1.4.1 Discrete and Continuous Time Notation I.1.4.2 Portfolio Holdings and Portfolio Weights I.1.4.3 Profit and Loss I.1.4.4 Percentage and Log Returns I.1.4.5 Geometric Brownian Motion I.1.4.6 Discrete and Continuous Compounding in Discrete Time I.1.4.7 Period Log Returns in Discrete Time I.1.4.8 Return on a Linear Portfolio I.1.4.9 Sources of Returns I.1.5 Functions of Several Variables I.1.5.1 Partial Derivatives: Function of Two Variables I.1.5.2 Partial Derivatives: Function of Several Variables xxiii 1 1 3 4 5 6 7 9 10 10 11 13 14 15 16 16 17 19 19 21 22 23 25 25 26 27 27 viii Contents I.1.5.3 Stationary Points I.1.5.4 Optimization I.1.5.5 Total Derivatives I.1.6 Taylor Expansion I.1.6.1 Definition and Examples I.1.6.2 Risk Factors and their Sensitivities I.1.6.3 Some Financial Applications of Taylor Expansion I.1.6.4 Multivariate Taylor Expansion I.1.7 Summary and Conclusions I.2 Essential Linear Algebra for Finance I.2.1 Introduction I.2.2 Matrix Algebra and its Mathematical Applications I.2.2.1 Basic Terminology I.2.2.2 Laws of Matrix Algebra I.2.2.3 Singular Matrices I.2.2.4 Determinants I.2.2.5 Matrix Inversion I.2.2.6 Solution of Simultaneous Linear Equations I.2.2.7 Quadratic Forms I.2.2.8 Definite Matrices I.2.3 Eigenvectors and Eigenvalues I.2.3.1 Matrices as Linear Transformations I.2.3.2 Formal Definitions I.2.3.3 The Characteristic Equation I.2.3.4 Eigenvalues and Eigenvectors of a 2 × 2 Correlation Matrix I.2.3.5 Properties of Eigenvalues and Eigenvectors I.2.3.6 Using Excel to Find Eigenvalues and Eigenvectors I.2.3.7 Eigenvalue Test for Definiteness I.2.4 Applications to Linear Portfolios I.2.4.1 Covariance and Correlation Matrices I.2.4.2 Portfolio Risk and Return in Matrix Notation I.2.4.3 Positive Definiteness of Covariance and Correlation Matrices I.2.4.4 Eigenvalues and Eigenvectors of Covariance and Correlation Matrices I.2.5 Matrix Decomposition I.2.5.1 Spectral Decomposition of a Symmetric Matrix I.2.5.2 Similarity Transforms I.2.5.3 Cholesky Decomposition I.2.5.4 LU Decomposition I.2.6 Principal Component Analysis I.2.6.1 Definition of Principal Components I.2.6.2 Principal Component Representation I.2.6.3 Case Study: PCA of European Equity Indices I.2.7 Summary and Conclusions 28 29 31 31 32 33 33 34 35 37 37 38 38 39 40 41 43 44 45 46 48 48 50 51 52 52 53 54 55 55 56 58 59 61 61 62 62 63 64 65 66 67 70 Contents I.3 Probability and Statistics I.3.1 Introduction I.3.2 Basic Concepts I.3.2.1 Classical versus Bayesian Approaches I.3.2.2 Laws of Probability I.3.2.3 Density and Distribution Functions I.3.2.4 Samples and Histograms I.3.2.5 Expected Value and Sample Mean I.3.2.6 Variance I.3.2.7 Skewness and Kurtosis I.3.2.8 Quantiles, Quartiles and Percentiles I.3.3 Univariate Distributions I.3.3.1 Binomial Distribution I.3.3.2 Poisson and Exponential Distributions I.3.3.3 Uniform Distribution I.3.3.4 Normal Distribution I.3.3.5 Lognormal Distribution I.3.3.6 Normal Mixture Distributions I.3.3.7 Student t Distributions I.3.3.8 Sampling Distributions I.3.3.9 Generalized Extreme Value Distributions I.3.3.10 Generalized Pareto Distribution I.3.3.11 Stable Distributions I.3.3.12 Kernels I.3.4 Multivariate Distributions I.3.4.1 Bivariate Distributions I.3.4.2 Independent Random Variables I.3.4.3 Covariance I.3.4.4 Correlation I.3.4.5 Multivariate Continuous Distributions I.3.4.6 Multivariate Normal Distributions I.3.4.7 Bivariate Normal Mixture Distributions I.3.4.8 Multivariate Student t Distributions I.3.5 Introduction to Statistical Inference I.3.5.1 Quantiles, Critical Values and Confidence Intervals I.3.5.2 Central Limit Theorem I.3.5.3 Confidence Intervals Based on Student t Distribution I.3.5.4 Confidence Intervals for Variance I.3.5.5 Hypothesis Tests I.3.5.6 Tests on Means I.3.5.7 Tests on Variances I.3.5.8 Non-Parametric Tests on Distributions I.3.6 Maximum Likelihood Estimation I.3.6.1 The Likelihood Function I.3.6.2 Finding the Maximum Likelihood Estimates I.3.6.3 Standard Errors on Mean and Variance Estimates ix 71 71 72 72 73 75 76 78 79 81 83 85 85 87 89 90 93 94 97 100 101 103 105 106 107 108 109 110 111 114 115 116 117 118 118 120 122 123 124 125 126 127 130 130 131 133 x Contents I.3.7 Stochastic Processes in Discrete and Continuous Time I.3.7.1 Stationary and Integrated Processes in Discrete Time I.3.7.2 Mean Reverting Processes and Random Walks in Continuous Time I.3.7.3 Stochastic Models for Asset Prices and Returns I.3.7.4 Jumps and the Poisson Process I.3.8 Summary and Conclusions I.4 Introduction to Linear Regression I.4.1 Introduction I.4.2 Simple Linear Regression I.4.2.1 Simple Linear Model I.4.2.2 Ordinary Least Squares I.4.2.3 Properties of the Error Process I.4.2.4 ANOVA and Goodness of Fit I.4.2.5 Hypothesis Tests on Coefficients I.4.2.6 Reporting the Estimated Regression Model I.4.2.7 Excel Estimation of the Simple Linear Model I.4.3 Properties of OLS Estimators I.4.3.1 Estimates and Estimators I.4.3.2 Unbiasedness and Efficiency I.4.3.3 Gauss–Markov Theorem I.4.3.4 Consistency and Normality of OLS Estimators I.4.3.5 Testing for Normality I.4.4 Multivariate Linear Regression I.4.4.1 Simple Linear Model and OLS in Matrix Notation I.4.4.2 General Linear Model I.4.4.3 Case Study: A Multiple Regression I.4.4.4 Multiple Regression in Excel I.4.4.5 Hypothesis Testing in Multiple Regression I.4.4.6 Testing Multiple Restrictions I.4.4.7 Confidence Intervals I.4.4.8 Multicollinearity I.4.4.9 Case Study: Determinants of Credit Spreads I.4.4.10 Orthogonal Regression I.4.5 Autocorrelation and Heteroscedasticity I.4.5.1 Causes of Autocorrelation and Heteroscedasticity I.4.5.2 Consequences of Autocorrelation and Heteroscedasticity I.4.5.3 Testing for Autocorrelation I.4.5.4 Testing for Heteroscedasticity I.4.5.5 Generalized Least Squares I.4.6 Applications of Linear Regression in Finance I.4.6.1 Testing a Theory I.4.6.2 Analysing Empirical Market Behaviour I.4.6.3 Optimal Portfolio Allocation 134 134 136 137 139 140 143 143 144 144 146 148 149 151 152 153 155 155 156 157 157 158 158 159 161 162 163 163 166 167 170 171 173 175 175 176 176 177 178 179 179 180 181 Contents I.4.6.4 Regression-Based Hedge Ratios I.4.6.5 Trading on Regression Models I.4.7 Summary and Conclusions xi 181 182 184 I.5 Numerical Methods in Finance I.5.1 Introduction I.5.2 Iteration I.5.2.1 Method of Bisection I.5.2.2 Newton–Raphson Iteration I.5.2.3 Gradient Methods I.5.3 Interpolation and Extrapolation I.5.3.1 Linear and Bilinear Interpolation I.5.3.2 Polynomial Interpolation: Application to Currency Options I.5.3.3 Cubic Splines: Application to Yield Curves I.5.4 Optimization I.5.4.1 Least Squares Problems I.5.4.2 Likelihood Methods I.5.4.3 The EM Algorithm I.5.4.4 Case Study: Applying the EM Algorithm to Normal Mixture Densities I.5.5 Finite Difference Approximations I.5.5.1 First and Second Order Finite Differences I.5.5.2 Finite Difference Approximations for the Greeks I.5.5.3 Finite Difference Solutions to Partial Differential Equations I.5.6 Binomial Lattices I.5.6.1 Constructing the Lattice I.5.6.2 Arbitrage Free Pricing and Risk Neutral Valuation I.5.6.3 Pricing European Options I.5.6.4 Lognormal Asset Price Distributions I.5.6.5 Pricing American Options I.5.7 Monte Carlo Simulation I.5.7.1 Random Numbers I.5.7.2 Simulations from an Empirical or a Given Distribution I.5.7.3 Case Study: Generating Time Series of Lognormal Asset Prices I.5.7.4 Simulations on a System of Two Correlated Normal Returns I.5.7.5 Multivariate Normal and Student t Distributed Simulations I.5.8 Summary and Conclusions 185 185 187 187 188 191 193 193 195 197 200 201 202 203 I.6 Introduction to Portfolio Theory I.6.1 Introduction I.6.2 Utility Theory I.6.2.1 Properties of Utility Functions I.6.2.2 Risk Preference I.6.2.3 How to Determine the Risk Tolerance of an Investor I.6.2.4 Coefficients of Risk Aversion 225 225 226 226 229 230 231 203 206 206 207 208 210 211 211 212 213 215 217 217 217 218 220 220 223 xii Contents I.6.2.5 I.6.2.6 I.6.2.7 I.6.3 I.6.4 I.6.5 I.6.6 Some Standard Utility Functions Mean–Variance Criterion Extension of the Mean–Variance Criterion to Higher Moments Portfolio Allocation I.6.3.1 Portfolio Diversification I.6.3.2 Minimum Variance Portfolios I.6.3.3 The Markowitz Problem I.6.3.4 Minimum Variance Portfolios with Many Constraints I.6.3.5 Efficient Frontier I.6.3.6 Optimal Allocations Theory of Asset Pricing I.6.4.1 Capital Market Line I.6.4.2 Capital Asset Pricing Model I.6.4.3 Security Market Line I.6.4.4 Testing the CAPM I.6.4.5 Extensions to CAPM Risk Adjusted Performance Measures I.6.5.1 CAPM RAPMs I.6.5.2 Making Decisions Using the Sharpe Ratio I.6.5.3 Adjusting the Sharpe Ratio for Autocorrelation I.6.5.4 Adjusting the Sharpe Ratio for Higher Moments I.6.5.5 Generalized Sharpe Ratio I.6.5.6 Kappa Indices, Omega and Sortino Ratio Summary and Conclusions 232 234 235 237 238 240 244 245 246 247 250 250 252 253 254 255 256 257 258 259 260 262 263 266 References 269 Statistical Tables 273 Index 279 List of Figures A linear function The quadratic function fx = 4x2 + 3x + 2 I.1.3 The reciprocal function I.1.4 The inverse of a function I.1.5 The exponential function I.1.6 The natural logarithmic function I.1.7 Definition of the first derivative I.1.8 Two functions I.1.9 The definite integral I.1.10 The h-period log return is the sum of h consecutive one-period log returns I.1.11 Graph of the function in Example I.1.8 I.2.1 A matrix is a linear transformation I.2.2 A vector that is not an eigenvector I.2.3 An eigenvector I.2.4 Six European equity indices I.2.5 The first principal component I.3.1 Venn diagram I.3.2 Density and distribution functions: (a) discrete random variable; (b) continuous variable I.3.3 Building a histogram in Excel I.3.4 The effect of cell width on the histogram shape I.3.5 Two densities with the same expectation I.1.1 I.1.2 4 5 6 7 8 I.3.6 9 I.3.8 10 12 15 I.3.9 24 27 I.3.7 I.3.10 I.3.11 I.3.12 I.3.13 48 I.3.14 49 50 67 I.3.15 I.3.16 69 75 I.3.17 77 78 I.3.18 78 I.3.19 I.3.20 but different standard deviations (a) A normal density and a leptokurtic density; (b) a positively skewed density The 0.1 quantile of a continuous random variable Some binomial density functions A binomial tree for a stock price evolution The standard uniform distribution Two normal densities Lognormal density associated with the standard normal distribution A variance mixture of two normal densities A skewed, leptokurtic normal mixture density Comparison of Student t densities and standard normal Comparison of Student t density and normal with same variance Comparison of standardized empirical density with standardized Student t density and standard normal density The Excel t distribution function Filtering data to derive the GEV distribution A Fréchet density 80 83 84 86 87 89 90 93 95 97 98 99 99 100 102 103 xiv List of Figures I.3.21 Filtering data in the peaks-over-threshold model I.3.22 Kernel estimates of S&P 500 returns I.3.23 Scatter plots from a paired sample of returns: (a) correlation +075; (b) correlation 0; (c) correlation −075 I.3.24 Critical regions for hypothesis tests I.3.25 The dependence of the likelihood on parameters I.3.26 The likelihood and the log likelihood functions I.3.27 FTSE 100 index I.3.28 Daily prices and log prices of DJIA index I.3.29 Daily log returns on DJIA index I.4.1 Scatter plot of Amex and S&P 500 daily log returns I.4.2 Dialog box for Excel regression I.4.3 Unbiasedness and efficiency I.4.4 Distribution of a consistent estimator I.4.5 Billiton share price, Amex Oil index and CBOE Gold index I.4.6 Dialog box for multiple regression in Excel I.4.7 The iTraxx Europe index and its determinants I.4.8 Residuals from the Billiton regression I.5.1 Method of bisection I.5.2 Setting Excel’s Goal Seek I.5.3 Newton–Raphson iteration I.5.4 104 107 I.5.5 I.5.6 I.5.7 I.5.8 I.5.9 113 I.5.10 125 I.5.11 130 I.5.12 131 133 I.5.13 I.5.14 137 138 I.5.15 I.5.16 145 I.5.17 153 I.5.18 156 I.5.19 157 I.5.20 162 I.6.1 164 I.6.2 172 I.6.3 178 187 189 189 I.6.4 I.6.5 Convergence of Newton–Raphson scheme Solver options Extrapolation of a yield curve Linear interpolation on percentiles Fitting a currency smile A cubic spline interpolated yield curve FTSE 100 and S&P 500 index prices, 1996–2007 Sterling–US dollar exchange rate, 1996–2007 Slope of chord about a point Discretization of space for the finite difference scheme A simple finite difference scheme A binomial lattice Computing the price of European and American puts Simulating from a standard normal distribution Possible paths for an asset price following geometric Brownian motion A set of three independent standard normal simulations A set of three correlated normal simulations Convex, concave and linear utility functions The effect of correlation on portfolio volatility Portfolio volatility as a function of portfolio weight Portfolio risk and return as a function of portfolio weight Minimum variance portfolio 190 191 193 195 197 200 204 204 206 209 210 210 216 218 220 221 222 229 239 241 242 243 I.6.6 I.6.7 I.6.8 Solver settings for Example I.6.9 The opportunity set and the efficient frontier Indifference curves of risk averse investor List of Figures xv Indifference curves of risk loving investor I.6.10 Market portfolio I.6.11 Capital market line I.6.12 Security market line 249 251 251 253 I.6.9 246 247 248 List of Tables I.1.1 Asset prices I.1.2 Portfolio weights and portfolio value I.1.3 Portfolio returns I.2.1 Volatilities and correlations I.2.2 The correlation matrix of weekly returns I.2.3 Eigenvectors and eigenvalues of the correlation matrix I.3.1 Example of the density of a discrete random variable I.3.2 Distribution function for Table I.3.1 I.3.3 Biased and unbiased sample moments I.3.4 The B(3, 1/6) distribution I.3.5 A Poisson density function I.3.6 A simple bivariate density I.3.7 Distribution of the product I.3.8 Calculating a covariance I.3.9 Sample statistics I.4.1 Calculation of OLS estimates I.4.2 Estimating the residual sum of sqaures and the standard error of the regression I.4.3 Estimating the total sum of squares I.4.4 Critical values of t3 I.4.5 Some of the Excel output for the Amex and S&P 500 model 18 18 26 56 68 68 75 75 82 86 88 110 110 111 127 147 149 150 152 154 I.4.6 ANOVA for the Amex and S&P 500 model I.4.7 Coefficient estimates for the Amex and S&P 500 model I.4.8 ANOVA for Billiton regression I.4.9 Wald, LM and LR statistics I.5.1 Mean and volatility of the FTSE 100 and S&P 500 indices and the £/$ FX rate I.5.2 Estimated parameters of normal mixture distributions I.5.3 Analytic vs finite difference Greeks I.5.4 Characteristics of asset returns I.6.1 Two investments (outcomes as returns) I.6.2 Two investments (utility of outcomes) I.6.3 Returns characteristics for two portfolios I.6.4 Two investments I.6.5 Sharpe ratio and weak stochastic dominance I.6.6 Returns on an actively managed fund and its benchmark I.6.7 Statistics on excess returns I.6.8 Sharpe ratios and adjusted Sharpe ratios I.6.9 Kappa indices 154 154 164 167 205 205 208 221 227 228 237 258 259 261 262 262 264 List of Examples I.1.1 I.1.2 I.1.3 I.1.4 I.1.5 I.1.6 I.1.7 I.1.8 I.1.9 I.1.10 I.1.11 I.2.1 I.2.2 I.2.3 I.2.4 I.2.5 I.2.6 I.2.7 I.2.8 I.2.9 I.2.10 I.2.11 Roots of a quadratic equation Calculating derivatives Identifying stationary points A definite integral Portfolio weights Returns on a long-short portfolio Portfolio returns Stationary points of a function of two variables Constrained optimization Total derivative of a function of three variables Taylor approximation Finding a matrix product using Excel Calculating a 4 × 4 determinant Finding the determinant and the inverse matrix using Excel Solving a system of simultaneous linear equations in Excel A quadratic form in Excel Positive definiteness Determinant test for positive definiteness Finding eigenvalues and eigenvectors Finding eigenvectors Using an Excel add-in to find eigenvectors and eigenvalues Covariance and correlation matrices 5 12 14 16 18 20 25 28 30 31 32 40 42 43 45 45 46 47 51 53 54 56 I.2.12 Volatility of returns and volatility of P&L I.2.13 A non-positive definite 3 × 3 matrix I.2.14 Eigenvectors and eigenvalues of a 2 × 2 covariance matrix I.2.15 Spectral decomposition of a correlation matrix I.2.16 The Cholesky matrix of a 2 × 2 matrix I.2.17 The Cholesky matrix of a 3 × 3 matrix I.2.18 Finding the Cholesky matrix in Excel I.2.19 Finding the LU decomposition in Excel I.3.1 Building a histogram I.3.2 Calculating moments of a distribution I.3.3 Calculating moments of a sample I.3.4 Evolution of an asset price I.3.5 Normal probabilities I.3.6 Normal probabilities for portfolio returns I.3.7 Normal probabilities for active returns I.3.8 Variance and kurtosis of a zero-expectation normal mixture I.3.9 Probabilities of normal mixture variables I.3.10 Calculating a covariance I.3.11 Calculating a correlation I.3.12 Normal confidence intervals 57 59 60 61 62 63 63 64 77 81 82 87 90 91 92 95 96 110 112 119 xviii List of Examples I.3.13 One- and two-sided confidence intervals I.3.14 Confidence interval for a population mean I.3.15 Testing for equality of means and variances I.3.16 Log likelihood of the normal density I.3.17 Fitting a Student t distribution by maximum likelihood I.4.1 Using the OLS formula I.4.2 Relationship between beta and correlation I.4.3 Estimating the OLS standard error of the regression I.4.4 ANOVA I.4.5 Hypothesis tests in a simple linear model I.4.6 Simple regression in matrix form I.4.7 Goodness-of-fit test in multiple regression I.4.8 Testing a simple hypothesis in multiple regression I.4.9 Testing a linear restriction I.4.10 Confidence interval for regression coefficient I.4.11 Prediction in multivariate regression I.4.12 Durbin–Watson test I.4.13 White’s heteroscedasticity test I.5.1 Excel’s Goal Seek I.5.2 Using Solver to find a bond yield I.5.3 Interpolating implied volatility I.5.4 Bilinear interpolation I.5.5 120 I.5.6 123 I.5.7 127 131 I.5.8 I.5.9 132 147 147 148 150 151 160 I.5.10 I.6.1 I.6.2 I.6.3 I.6.4 I.6.5 I.6.6 164 165 165 168 I.6.7 I.6.8 I.6.9 169 177 I.6.10 I.6.11 177 188 I.6.12 191 I.6.13 I.6.14 194 194 I.6.15 Fitting a 25-delta currency option smile Interpolation with cubic splines Finite difference approximation to delta, gamma and vega Pricing European call and put options Pricing an American option with a binomial lattice Simulations from correlated Student t distributed variables Expected utility Certain equivalents Portfolio allocations for an exponential investor Higher moment criterion for an exponential investor Minimum variance portfolio: two assets Minimum variance portfolio on S&P 100 and FTSE 100 General formula for minimum variance portfolio The Markowitz problem Minimum variance portfolio with many constraints The CML equation Stochastic dominance and the Sharpe ratio Adjusting a Sharpe ratio for autocorrelation Adjusted Sharpe ratio Computing a generalized Sharpe ratio Omega, Sortino and kappa indices 196 198 208 212 215 222 227 228 235 236 241 242 244 245 246 252 258 260 261 263 264 Foreword How many children dream of one day becoming risk managers?

What is our best guess of data that are outside our range of observations? For instance, suppose the monthly spot rates from 1 month to 36 months are as shown in Figure I.5.6. How should we ‘extrapolate’ these data to obtain the spot rates up to 48 months? Since the yield curve is not a straight line, we need to fit a quadratic or higher order polynomial in order to extrapolate to the longer maturities. UK Short Spot Curve, 31 May 2002 5.50 5.25 5.00 4.75 4.50 4.25 4.00 3.75 3.50 0 4 m 8 m 12 m 16 m 20 m 24 m 28 m 32 m 36 m 40 m 44 m Figure I.5.6 Extrapolation of a yield curve I.5.3.1 Linear and Bilinear Interpolation Given two data points, x1 y1 and x2 y2 with x1 < x2 , linear interpolation gives the value of y at some point x ∈ x1 x2 as x − x1 y1 + x − x1 y2 y= 2 (I.5.11) x2 − x1 7 See http://en.wikipedia.org/wiki/Conjugate_gradient_method. 194 Quantitative Methods in Finance For an example of linear interpolation, consider the construction of a constant maturity 30-day futures series from traded futures.

If further data on 10-delta strangles and risk reversals are available, two more points can be added to the implied volatility smile: 10 = 50 + ST10 + 21 RR10 90 = 50 + ST10 − 21 RR10 (I.5.13) A more precise interpolation and extrapolation method is then to fit a quartic polynomial to the ATM, 25-delta and 10-delta data: this is left as an exercise to the reader. I.5.3.3 Cubic Splines: Application to Yield Curves Spline interpolation is a special type of piecewise polynomial interpolation that is usually more accurate than ordinary polynomial interpolation, even when the spline polynomials have quite low degree. In this section we consider cubic splines, since these are the lowest degree splines with attractive properties and are in use by many financial institutions, for instance for yield curve fitting and for volatility smile surface interpolation. We aim to interpolate a function fx using a cubic spline. First a series of knot points x1 xm are fixed. Then a cubic polynomial is fitted between consecutive knot points.


pages: 537 words: 144,318

The Invisible Hands: Top Hedge Fund Traders on Bubbles, Crashes, and Real Money by Steven Drobny

Albert Einstein, Asian financial crisis, asset allocation, asset-backed security, backtesting, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, business cycle, business process, buy and hold, capital asset pricing model, capital controls, central bank independence, collateralized debt obligation, commoditize, Commodity Super-Cycle, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, diversification, diversified portfolio, equity premium, family office, fiat currency, fixed income, follow your passion, full employment, George Santayana, Hyman Minsky, implied volatility, index fund, inflation targeting, interest rate swap, inventory management, invisible hand, Kickstarter, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, market microstructure, moral hazard, Myron Scholes, North Sea oil, open economy, peak oil, pension reform, Ponzi scheme, prediction markets, price discovery process, price stability, private sector deleveraging, profit motive, purchasing power parity, quantitative easing, random walk, reserve currency, risk tolerance, risk-adjusted returns, risk/return, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, special drawing rights, statistical arbitrage, stochastic volatility, stocks for the long run, stocks for the long term, survivorship bias, The Great Moderation, Thomas Bayes, time value of money, too big to fail, transaction costs, unbiased observer, value at risk, Vanguard fund, yield curve, zero-sum game

For example, we were ahead of the game in predicting that the yield curve would invert during the Greenspan conundrum era (see box on page 276). We realized that the world had switched from one of supply constriction in commodities to one of demand pull, and that a bull market in commodities (with the associated switch from backwardation into contango in commodity futures curves) would be reflected in an inverted yield curve. In a deflationary consumer environment with an inflationary real asset environment, the real asset inflationary aspects affect the short end of the curve, but the long end remains locked down. With productivity gains and no real inflation feeding through to core CPI—because core excludes food and energy—we bought bonds on the long end and put on yield curve inversion trades, which practically everyone scoffed at.

It’s easy to replicate and model portfolios with leveraged government bond positions or bond option positions, and the most interesting thing about this kind of leverage is that historically, it adds value to portfolios. It’s like receiving free insurance because the bond risk premium is positive. If you buy a portfolio of government bonds and fund it by borrowing cash, if there is an upward sloping yield curve on average, over the business cycle or over any long period of time, that portfolio will make money. Hence you are buying insurance that, on average, makes you money. It is an incredibly interesting idea because normally insurance costs you money. In 2007, we were looking at all kinds of things to hedge an equity portfolio in a bad event. Credit spreads were tight, so we viewed betting on credit spread wideners as an out-of-the-money put on stocks.

For an example of an asymmetric bet available today, look at Sweden. The central bank of Sweden, the Riksbank, has announced they do not plan to raise rates before June 2010. The one-year interest rate is currently 87 basis points, whereas the one-year interest rate in one year’s time is 2.52 percent, and the one-year interest rate in two years’ time is 3.50 percent. Right now, due to the steep upward sloping forward yield curve, you can buy receiver swaptions on one-year interest rates struck at 1.8 percent that will increase in value six times if one-year interest rates remain unchanged. This is not a bad payout for a world with very low rates because the global economy remains weak or stock markets have sold off again. These examples call for real money investment committees to widen their search for risk premia beyond the usual assets covered, and be willing to use option-like derivatives to purchase potential upside for a portfolio that works especially well during periods of crisis.


pages: 819 words: 181,185

Derivatives Markets by David Goldenberg

Black-Scholes formula, Brownian motion, capital asset pricing model, commodity trading advisor, compound rate of return, conceptual framework, correlation coefficient, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, en.wikipedia.org, financial innovation, fudge factor, implied volatility, incomplete markets, interest rate derivative, interest rate swap, law of one price, locking in a profit, London Interbank Offered Rate, Louis Bachelier, margin call, market microstructure, martingale, Myron Scholes, Norbert Wiener, Paul Samuelson, price mechanism, random walk, reserve currency, risk/return, riskless arbitrage, Sharpe ratio, short selling, stochastic process, stochastic volatility, time value of money, transaction costs, volatility smile, Wiener process, yield curve, zero-coupon bond, zero-sum game

To do so, we need the appropriate discount rates. 8.9.2 Valuation of the Fixed-Rate Bond In order to value the fixed-rate bond in which the dealer is long, we need the appropriate discount rates to apply to the bond’s cash flows. We are in the world of interest-rate swaps which is a LIBOR world. So we need the current (t=0) spot LIBOR yield curve. It gives the rates to be applied to zero-coupon Eurobonds for alternative maturities. Assume that it is as in Table 8.8. Our long position in the fixed-rate bond can be decomposed as the sum of three zero-coupon bonds, and one NP repayment bond as indicated in the multi-level cash flow diagram, Figure 8.15. TABLE 8.8 LIBOR Yield Curve (Spot Rates) Maturity Zero-Coupon Bond Yields 1 year 6.0% 2 years 6.5% 3 years 7.0% FIGURE 8.15 The Implicit Fixed-Rate Bond in a Swap, Written in Terms of Zero-Coupon Bonds The LIBOR zero-yield curve says that the appropriate discount rate to apply to the cash flow from Bond 1 is 6.00%, the appropriate discount rate to apply to the cash flow from Bond 2 is 6.5%, and the appropriate discount rate to apply to the cash flows from Bond 3 and from Bond 4 is 7.0%.

The weights add up to 1.0 and are the current prices of 1, 2, and 3-year unit discount LIBOR bonds each expressed as a percentage of the sum of the values of those bonds, Interpretation 2 This representation is equivalent to that given in section 8.9.4 of the par swap rate as To establish this equivalence, all we have to do is to show that, Re-write LIBOR1,0 as r1 ,where r1 is the LIBOR zero yield curve rate used to price is the LIBOR zero yield curve rate used to price , and r3 is the LIBOR zero yield curve rate used to price . Using the definitions of the IFRs we obtain that the right hand side of the required equality, , is equal to, This is what we wanted to demonstrate because it is the left side of Interpretation 3 There is a third useful interpretation of the par swap rate that follows from basic bond finance. The short (seller) in a swap (the dealer counterparty in the BBB example) has issued (shorted) a floating-rate bond and invested in (longed) a fixed-rate bond.

Then we can define the important notion of the Implied Forward Rate (IFR) on one-year loans one year from today as the artificial, non-random rate that equates the expected payoffs of strategies 1. and 2. That is, it is the rate, denoted by IFR1,1, such that (1+LIBOR2,0)2= (1+LIBOR1,0)*(1+IFR1,1). The IFR1,1 is given by, Similarly, the Implied Forward Rate on one-year loans two years from today, denoted by IFR1,2, is defined as the artificial rate such that, Implied Forward Rates are obtained from the LIBOR yield curve, or from the prices of Eurodollar futures contracts. For example, based on the LIBOR yield curve given in Table 8.8, we can imply 1-year forward rates one year from today and two years from today. ■ CONCEPT CHECK 6 a. Calculate, based upon Table 8.8, the IFR for 1-year loans one year from today, IFR1,1. b. Also, calculate the IFR for 1-year loans two years from today, IFR1,2. Implied Forward Rates are useful because, under certain assumptions, they are unbiased estimates of future 1-year LIBOR.


pages: 586 words: 159,901

Wall Street: How It Works And for Whom by Doug Henwood

accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, affirmative action, Andrei Shleifer, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, borderless world, Bretton Woods, British Empire, business cycle, capital asset pricing model, capital controls, central bank independence, computerized trading, corporate governance, corporate raider, correlation coefficient, correlation does not imply causation, credit crunch, currency manipulation / currency intervention, David Ricardo: comparative advantage, debt deflation, declining real wages, deindustrialization, dematerialisation, diversification, diversified portfolio, Donald Trump, equity premium, Eugene Fama: efficient market hypothesis, experimental subject, facts on the ground, financial deregulation, financial innovation, Financial Instability Hypothesis, floating exchange rates, full employment, George Akerlof, George Gilder, hiring and firing, Hyman Minsky, implied volatility, index arbitrage, index fund, information asymmetry, interest rate swap, Internet Archive, invisible hand, Irwin Jacobs, Isaac Newton, joint-stock company, Joseph Schumpeter, kremlinology, labor-force participation, late capitalism, law of one price, liberal capitalism, liquidationism / Banker’s doctrine / the Treasury view, London Interbank Offered Rate, Louis Bachelier, market bubble, Mexican peso crisis / tequila crisis, microcredit, minimum wage unemployment, money market fund, moral hazard, mortgage debt, mortgage tax deduction, Myron Scholes, oil shock, Paul Samuelson, payday loans, pension reform, plutocrats, Plutocrats, price mechanism, price stability, prisoner's dilemma, profit maximization, publication bias, Ralph Nader, random walk, reserve currency, Richard Thaler, risk tolerance, Robert Gordon, Robert Shiller, Robert Shiller, selection bias, shareholder value, short selling, Slavoj Žižek, South Sea Bubble, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Market for Lemons, The Nature of the Firm, The Predators' Ball, The Wealth of Nations by Adam Smith, transaction costs, transcontinental railway, women in the workforce, yield curve, zero-coupon bond

For traders in global firms, the trading day begins in Tokyo; they "pass the book" at about 4 or 5 in the afternoon Tokyo time to London, where it is 7 or 8 in the morning, and pass it westwards at 1 PM to New York, where it is 8 in the morning. The trading day ends when New York closes. Treasury debt falls into three categories — bills, with maturities running from three months to one year; notes, one year to 10; and bonds, over 10. Most trading occurs in the two- to seven-year range. Shown on p. 26 are three "yield curves," plots of interest rates at various maturities. Normally the yield curve slopes gently upward, with interest rates rising as maturities lengthen. The reason for this is pretty simple — the longer a maturity, the more possibility there is for something to go wrong (inflation, financial panic, war), so investors require a sweeter return to tempt them into parting with their money. It's rare, however, that a bondholder would actually hold it to maturity; holding periods of weeks and hours are more common than years.

After a few months of declining rates — usually encouraged by the Federal Reserve — the stock market begins reponding to one of its favorite stimuli, lower rates. At business cycle peaks, the process is thrown into reverse gear, with interest rates steadily rising and the stock market flattening and finally sinking. Note that short-term rates move far more dramatically in both directions than long-term rates; the yield curve normally flattens or even goes negative as the economy approaches recession, then turns steeply positive as the slowdown ends. In fact, the yield curve "significantly outperforms other financial and macroeconomic indicators in predicting recessions two to six quarters ahead" (Estrella and Mishkin 1996) — and not only in the U.S., but in most of the rich industrial countries (Bernard and Gerlach 1996). It outdoes the stock market and composite leading indicators at this distance, with stocks and the composites having a forward vision of only about a single quarter.^ The bond market's fear of economic strength, and its love of weakness, used to be something of a Wall Street secret, at least until around 1993, when it became a more open secret.

In the early 1980s, the curve was negative, as Volcker's Fed drove rates up to record levels to kill inflation; in the early 1990s, it was quite steep, and Greenspan's Fed forced rates down to keep the financial system from imploding. It's likely that investors assumed that both extremes were not sustainable, and that short rates would return to more "normal" levels, which is why the longer end of the curve never got so carried away. 16% 14% 12% 10% 8% 6% 4% 2% 0% U.S. Treasury yield curves Jan 1997 Dec 1980 Oct 1992 3-mo 1 2 3 5 7 10 30 years to maturity mums Federal government bonds aren't the only kind, of course. Cities and states sell tax-exempt municipal bonds, which help retired dentists to shelter income and local governments to build sewers and subsidize shopping malls in the name of "industrial development." The muni bond market is smaller than the U.S. Treasury market — at the end of 1997, state and local governments had $1.1 trillion in debt outstanding, compared to $3.8 trillion for the Treasury and another $2.7 trillion for government-related financial institutions — and trading is usually sleepy and uninteresting.


pages: 317 words: 106,130

The New Science of Asset Allocation: Risk Management in a Multi-Asset World by Thomas Schneeweis, Garry B. Crowder, Hossein Kazemi

asset allocation, backtesting, Bernie Madoff, Black Swan, business cycle, buy and hold, capital asset pricing model, collateralized debt obligation, commodity trading advisor, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, fixed income, high net worth, implied volatility, index fund, interest rate swap, invisible hand, market microstructure, merger arbitrage, moral hazard, Myron Scholes, passive investing, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, statistical model, stocks for the long run, survivorship bias, systematic trading, technology bubble, the market place, Thomas Kuhn: the structure of scientific revolutions, transaction costs, value at risk, yield curve, zero-sum game

First, the signals must make economic sense; that is, one should be able to explain in simple terms why the model is able to forecast relative performance of various asset classes. For example, one of the most reliable signals about future performance of equities relative to fixed income instruments has been the slope of the yield curve. An upward sloping yield curve is generally consistent with a period of rising stock prices. The reason behind this is that an upward sloping yield curve is generally observed at the beginning of economic expansion. By examining the economic foundation of the signal, one can avoid using results that have resulted from data mining, that is, the generated signals that are not likely to perform well out of sample. So what are the economic foundations of a sensible signaling model of a TAA?

For many, risk is defined as any factor that may lead to the possibility of losing some or all of an investment as well as the magnitude and duration of that loss, while portfolio standard deviation centers on the probability of loss. However, for those who focus on risk measures beyond standard deviation of return, risk analysis at the portfolio level includes a wide range of analysis, including:9 34 ■ ■ ■ ■ ■ ■ THE NEW SCIENCE OF ASSET ALLOCATION Market Risk Analysis (changes in the yield curve or other marketrelated variables) on the performance of the portfolio as well as the primary asset sectors. Changes in factors such as interest rate movements, yield curve shifts, and other economic factors provide additional information on the macro sensitivity of the portfolio to economic factors. Performance Attribution: Attribution analysis, which measures the sources of return on an asset class as well as sector selection as a percentage of total return. Correlation Analysis: Correlation within an asset class (e.g., strategies, security sectors, geographical regions) and across asset classes.

How they could or should be priced in a single-factor or even a multi-factor model framework was explored, but a solution was rarely found.9 Option Pricing Models and Growth of Futures Markets We have spent a great deal of time focusing on the equity markets. During this period of market innovation, considerable research also centered on direct arbitrage relationships. Arbitrage relationships in capital and A Brief History of Asset Allocation 11 corporate markets were explored during the 1930s (forward interest rates implied in yield curve models)10 and in the 1950s (corporate dividend policy and debt policy). Similarly, cost of carry arbitrage models had long been the focal point of pricing in most futures based research. In the early 1970s Fischer Black and Myron Scholes (1973) and Merton (1973) developed a simple-to-use option pricing model based in part on arbitrage relationships between investment vehicles. Soon after, fundamental arbitrage between the relative prices of a put option (the right to sell) and a call option (the right to buy) formed a process to become known as the Put-Call Parity Model, which provided a means to explain easily the various ways options can be used to modify the underlying risk characteristics of existing portfolios.


pages: 374 words: 94,508

Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage by Douglas B. Laney

3D printing, Affordable Care Act / Obamacare, banking crisis, blockchain, business climate, business intelligence, business process, call centre, chief data officer, Claude Shannon: information theory, commoditize, conceptual framework, crowdsourcing, dark matter, data acquisition, digital twin, discounted cash flows, disintermediation, diversification, en.wikipedia.org, endowment effect, Erik Brynjolfsson, full employment, informal economy, intangible asset, Internet of things, linked data, Lyft, Nash equilibrium, Network effects, new economy, obamacare, performance metric, profit motive, recommendation engine, RFID, semantic web, smart meter, Snapchat, software as a service, source of truth, supply-chain management, text mining, uber lyft, Y2K, yield curve

This leads us to a examination of information yield (a concept loosely inspired by the traditional yield curve6) for expressing the rate of improved value per unit of information-related investment. The information yield curve brings together, conceptually, the concepts of information monetization, management, and measurement. As we all know, information asset management (IAM) involves numerous moving parts. And as enumerated in chapter 5, many factors exert upward or downward pressure on information maturity. But what does this maturation curve look like? And where are you on it? The information yield curve which my Gartner colleagues Andrew White, Joe Bugajski, Frank Buytendijk, and I devised a few years ago is intended to answer these questions. Not computationally, but more along the lines of how the popular Gartner Hype Cycle7 sets maturity expectations for technology users and suppliers, the information yield curve sets expectations for how information-related investments affect IAM maturity—and thereby your information’s rate of return.

How understanding the marginal utility of information for both human and technology-based consumers of information should drive business and architecture decisions. How the opportunity costs of certain information assets must be factored into selecting and publishing them. How the information production possibility frontier affects information-related behavior and investments. How to use Gartner’s information yield curve to conceptually integrate the concepts of information monetization, management, and measurement for improved information-related and business strategies. The Supply and Demand of Information Information is an unruly asset. As pointed out earlier, it does not deplete when consumed, it can be used simultaneously, it is representative of some other entity or activity, it costs comparatively little to store or transmit, and it can instantly transform or disappear.

Similarly, low-maturity organizations that accelerate their information-related investments will experience a maturity bump that begins to level off. For example, moving to a more sophisticated analytics platform will have quicker returns initially until your competitors catch up, market dynamics change, or the platform becomes overwhelmed by the increased volume, velocity, and/or variety of information assets—such as we have observed in recent years with traditional data warehousing approaches. Figure 12.1 Information Yield Curve The forces exerting upward pressure to improve and accelerate maturity should be embraced and cultivated. Many of these can be found in the information management maturity challenges mentioned in chapter 5, such as information quality, accessibility, compliance, analytics, governance, and so forth. The downward forces that limit and tend to decelerate IAM maturity include the volume, velocity, and variety of information, the increasing ubiquity of information assets, the speed of business, regulatory mandates, organizational resistance/inertia, information hoarding and underutilization, and lack of information trust (metadata).


pages: 416 words: 39,022

Asset and Risk Management: Risk Oriented Finance by Louis Esch, Robert Kieffer, Thierry Lopez

asset allocation, Brownian motion, business continuity plan, business process, capital asset pricing model, computer age, corporate governance, discrete time, diversified portfolio, fixed income, implied volatility, index fund, interest rate derivative, iterative process, P = NP, p-value, random walk, risk/return, shareholder value, statistical model, stochastic process, transaction costs, value at risk, Wiener process, yield curve, zero-coupon bond

If we describe P (s) as the issue price of a zero-coupon bond with maturity s and R(s) as the rate observed on the market at moment 0 for this type of security, called the spot rate, these two values are clearly linked by the relation P (s) = (1 + R(s))−s . The value R(s), for all the values of s > 0, constitutes the term interest-rate structure at moment 0 and the graph for this function is termed the yield curve. The most natural direction of the yield curve is of course upwards; the investor should gain more if he invests over a longer period. This, however, is not always the case; in practice we frequently see flat curves (constant R(s) value) as well as increasing curves, as well as inverted curves (decreasing R(s) value) and humped curves (see Figure 4.4). R(s) R(s) s R(s) s R(s) s s Figure 4.4 Interest rate curves 13 A detailed presentation of these concepts can be found in Bisière C., La Structure par Terme des Taux d’intérêt, Presses Universitaires de France, 1997. 14 This justifies the title of this present section, which mentions ‘interest rates’ and not bonds. 130 Asset and Risk Management 4.3.2 Static interest rate structure The static models examine the structure of interest rates at a fixed moment, which we will term 0, and deal with a zero-coupon bond that gives rise to a repayment of 1, which is not a restriction.

xix xix xxi PART I THE MASSIVE CHANGES IN THE WORLD OF FINANCE Introduction 1 The Regulatory Context 1.1 Precautionary surveillance 1.2 The Basle Committee 1.2.1 General information 1.2.2 Basle II and the philosophy of operational risk 1.3 Accounting standards 1.3.1 Standard-setting organisations 1.3.2 The IASB 2 Changes in Financial Risk Management 2.1 Definitions 2.1.1 Typology of risks 2.1.2 Risk management methodology 2.2 Changes in financial risk management 2.2.1 Towards an integrated risk management 2.2.2 The ‘cost’ of risk management 2.3 A new risk-return world 2.3.1 Towards a minimisation of risk for an anticipated return 2.3.2 Theoretical formalisation 1 2 3 3 3 3 5 9 9 9 11 11 11 19 21 21 25 26 26 26 vi Contents PART II EVALUATING FINANCIAL ASSETS Introduction 3 4 29 30 Equities 3.1 The basics 3.1.1 Return and risk 3.1.2 Market efficiency 3.1.3 Equity valuation models 3.2 Portfolio diversification and management 3.2.1 Principles of diversification 3.2.2 Diversification and portfolio size 3.2.3 Markowitz model and critical line algorithm 3.2.4 Sharpe’s simple index model 3.2.5 Model with risk-free security 3.2.6 The Elton, Gruber and Padberg method of portfolio management 3.2.7 Utility theory and optimal portfolio selection 3.2.8 The market model 3.3 Model of financial asset equilibrium and applications 3.3.1 Capital asset pricing model 3.3.2 Arbitrage pricing theory 3.3.3 Performance evaluation 3.3.4 Equity portfolio management strategies 3.4 Equity dynamic models 3.4.1 Deterministic models 3.4.2 Stochastic models 35 35 35 44 48 51 51 55 56 69 75 79 85 91 93 93 97 99 103 108 108 109 Bonds 4.1 Characteristics and valuation 4.1.1 Definitions 4.1.2 Return on bonds 4.1.3 Valuing a bond 4.2 Bonds and financial risk 4.2.1 Sources of risk 4.2.2 Duration 4.2.3 Convexity 4.3 Deterministic structure of interest rates 4.3.1 Yield curves 4.3.2 Static interest rate structure 4.3.3 Dynamic interest rate structure 4.3.4 Deterministic model and stochastic model 4.4 Bond portfolio management strategies 4.4.1 Passive strategy: immunisation 4.4.2 Active strategy 4.5 Stochastic bond dynamic models 4.5.1 Arbitrage models with one state variable 4.5.2 The Vasicek model 115 115 115 116 119 119 119 121 127 129 129 130 132 134 135 135 137 138 139 142 Contents 4.5.3 The Cox, Ingersoll and Ross model 4.5.4 Stochastic duration 5 Options 5.1 Definitions 5.1.1 Characteristics 5.1.2 Use 5.2 Value of an option 5.2.1 Intrinsic value and time value 5.2.2 Volatility 5.2.3 Sensitivity parameters 5.2.4 General properties 5.3 Valuation models 5.3.1 Binomial model for equity options 5.3.2 Black and Scholes model for equity options 5.3.3 Other models of valuation 5.4 Strategies on options 5.4.1 Simple strategies 5.4.2 More complex strategies PART III GENERAL THEORY OF VaR Introduction vii 145 147 149 149 149 150 153 153 154 155 157 160 162 168 174 175 175 175 179 180 6 Theory of VaR 6.1 The concept of ‘risk per share’ 6.1.1 Standard measurement of risk linked to financial products 6.1.2 Problems with these approaches to risk 6.1.3 Generalising the concept of ‘risk’ 6.2 VaR for a single asset 6.2.1 Value at Risk 6.2.2 Case of a normal distribution 6.3 VaR for a portfolio 6.3.1 General results 6.3.2 Components of the VaR of a portfolio 6.3.3 Incremental VaR 181 181 181 181 184 185 185 188 190 190 193 195 7 VaR Estimation Techniques 7.1 General questions in estimating VaR 7.1.1 The problem of estimation 7.1.2 Typology of estimation methods 7.2 Estimated variance–covariance matrix method 7.2.1 Identifying cash flows in financial assets 7.2.2 Mapping cashflows with standard maturity dates 7.2.3 Calculating VaR 7.3 Monte Carlo simulation 7.3.1 The Monte Carlo method and probability theory 7.3.2 Estimation method 199 199 199 200 202 203 205 209 216 216 218 viii Contents 7.4 Historical simulation 7.4.1 Basic methodology 7.4.2 The contribution of extreme value theory 7.5 Advantages and drawbacks 7.5.1 The theoretical viewpoint 7.5.2 The practical viewpoint 7.5.3 Synthesis 8 Setting Up a VaR Methodology 8.1 Putting together the database 8.1.1 Which data should be chosen?

As the second-degree term C(r)2 /2 of the approximation formula is always positive, it therefore appears that when one has to choose between two bonds with the same return (actuarial rate) and duration, it will be preferable to choose the one with Bonds 129 the greater convexity regardless of the direction of the potential variation in the rate of return. 4.3 DETERMINISTIC STRUCTURE OF INTEREST RATES13 4.3.1 Yield curves The actuarial rate at the issue of a bond, as defined in Section 4.1.2 is obviously a particular characteristic to the security in question. The rate will vary from one bond to another, depending mainly on the quality of the issuer (assessed using the ratings issued by public rating companies) and the maturity of the security. The first factor is of course very difficult to model, and we will not be taking account of it, assuming throughout this section 4.3 that we are dealing with a public issuer who does not carry any risk of default.


pages: 272 words: 19,172

Hedge Fund Market Wizards by Jack D. Schwager

asset-backed security, backtesting, banking crisis, barriers to entry, beat the dealer, Bernie Madoff, Black-Scholes formula, British Empire, business cycle, buy and hold, Claude Shannon: information theory, cloud computing, collateralized debt obligation, commodity trading advisor, computerized trading, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, diversification, diversified portfolio, Edward Thorp, family office, financial independence, fixed income, Flash crash, hindsight bias, implied volatility, index fund, intangible asset, James Dyson, Jones Act, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, money market fund, oil shock, pattern recognition, pets.com, Ponzi scheme, private sector deleveraging, quantitative easing, quantitative trading / quantitative finance, Right to Buy, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Rubik’s Cube, Sharpe ratio, short selling, statistical arbitrage, Steve Jobs, systematic trading, technology bubble, transaction costs, value at risk, yield curve

For example, after liquidity dried up in the money markets in August 2007, O’Shea expected rates to be cut. Instead of expressing this trade idea only through long short-term interest rate instrument positions, O’Shea also implemented the trade as a yield curve spread: long short-term rate instruments/short long-term rate instruments. His reasoning was that the yield curve at the time was relatively flat, implying that a rate decline would most likely be concentrated on the short-term end of the yield curve. If, however, rates went up, the flat yield curve implied that long-term rates should go up at least as much as short-term rates and probably more. The yield curve spread provided most of the profit potential with only a fraction of the risk. In essence, it provided a much better return-to-risk ratio than a straight long position in short-term rates alone.

What did Greenspan do after the 1987 crash? He injected liquidity. Right. Add liquidity and cut rates. That was the policy response we expected. So that was our trade at the time: Rates would go lower and the yield curve would steepen. So you put on long positions in short-term rate instruments? Yes, but we coupled it with short positions on the long end because it was a better risk/reward trade. The yield curve was flat at the time and priced to stay flat. The market wasn’t pricing in any risk that there would be a major problem. So you bet on lower short-term rates through a yield curve spread rather than a long position in short-term rate instruments because you felt it was a safer way to do the trade. Yes, because what I am trying to do is find trades that won’t lose much money even if I am wrong.

Bull markets ignore any bad news, and any good news is a reason for a further rally. Can you think of an example where the market response to the news was counter to what you expected and impacted your trade? In 2009, I was long 2-year notes/short 10-year notes one-year forward, looking for the yield curve to widen, and a lot of news came out that I thought would hurt me. One news item after another, I saw the screen and thought, I am going to get screwed in this position. But I didn’t. After a number of these instances, I thought, the yield curve just can’t get any flatter no matter what comes out. So I quadrupled my position. It was a great trade. The spread went from 25 basis points to 210, although I got out at 110. Any other examples where the market action was the catalyst for a trade? When the whole debt fiasco in Europe started to unfold, the euro plunged from 1.45 to 1.19.


How I Became a Quant: Insights From 25 of Wall Street's Elite by Richard R. Lindsey, Barry Schachter

Albert Einstein, algorithmic trading, Andrew Wiles, Antoine Gombaud: Chevalier de Méré, asset allocation, asset-backed security, backtesting, bank run, banking crisis, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, Brownian motion, business cycle, business process, butter production in bangladesh, buy and hold, buy low sell high, capital asset pricing model, centre right, collateralized debt obligation, commoditize, computerized markets, corporate governance, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, discounted cash flows, disintermediation, diversification, Donald Knuth, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, full employment, George Akerlof, Gordon Gekko, hiring and firing, implied volatility, index fund, interest rate derivative, interest rate swap, John von Neumann, linear programming, Loma Prieta earthquake, Long Term Capital Management, margin call, market friction, market microstructure, martingale, merger arbitrage, Myron Scholes, Nick Leeson, P = NP, pattern recognition, Paul Samuelson, pensions crisis, performance metric, prediction markets, profit maximization, purchasing power parity, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Richard Feynman, Richard Stallman, risk-adjusted returns, risk/return, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, sorting algorithm, statistical arbitrage, statistical model, stem cell, Steven Levy, stochastic process, systematic trading, technology bubble, The Great Moderation, the scientific method, too big to fail, trade route, transaction costs, transfer pricing, value at risk, volatility smile, Wiener process, yield curve, young professional

If the men on the trading floor had had any idea that this activity would evolve from a small P&L source to a major business, or be as “sexy” as the M&A business, then I would have not been given the chance. We hired an academic to build a pricing model. The model provided Black-Scholes type prices, but with a couple of simplifications. To be able to obtain real-time prices at the beginning of each day, the head trader (me) had to select the values to assign to each of two parameters. The first parameter was yield-curve shape, and the choices of parameter values were “relatively flat” and “relatively steep.” The second parameter was spot volatility (no JWPR007-Lindsey 90 May 28, 2007 15:39 h ow i b e cam e a quant vol surface here!). The choices were “relatively high” and “relatively low.” This model was more sophisticated at the time than models used elsewhere in the bank and among our competitors for cap pricing.

I believe that I was the first in the industry to answer this question with the delta-gamma approach (see Wilson (1994b)), based on the observation that a quadratic form of normal variables is distributed as the sum of noncentral chi-squared variables for which numerical solutions are available. How many independent factors are practically required to capture the risk of a multicurrency fixed income trading book? The dimensionality of VaR calculations for a global trading book quickly becomes too large to be calculated efficiently, especially if each point on the yield curve is modeled separately. A logical place to look for a reduction in the dimensionality was therefore in the modeling of multicurrency interest rates. My answer (see Wilson (1994a)) compared both factor analysis and eigenvalue decompositions of multicurrency term structures and attempted to characterize the required number of factors and the stability of the parameter estimates over time. What happens to the tails of our VaR calculations if we have only estimates of volatilities and correlations and not their exact values?

It was an exciting project, and he had put together a strong team for the job. I guess it was for most of us the first time that we had been involved in building what amounted to a whole derivatives pricing JWPR007-Lindsey 174 May 18, 2007 21:24 h ow i b e cam e a quant system from scratch. Everything had to be coded from the ground up: ISDA day counting and accrual conventions, holiday calendar handling, volatility quotation conventions, settlement delays, yield curve stripping, simple analytical convexity corrections, a whole host of simple option analytics (the usual suspects: baskets, barriers, etc.), number generators, multithreaded Monte Carlo simulation, variance reduction techniques, multidimensional tree solvers for diffusion-based models, general finite differencing solvers for jump-diffusion based models, multifactor HullWhite models, LIBOR market models with global calibration, Bermudan Monte Carlo techniques, serialization of any of model or product objects for possible storage or distribution, distributed valuation framework, etc.


pages: 782 words: 187,875

Big Debt Crises by Ray Dalio

Asian financial crisis, asset-backed security, bank run, banking crisis, basic income, Ben Bernanke: helicopter money, break the buck, Bretton Woods, British Empire, business cycle, capital controls, central bank independence, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, declining real wages, European colonialism, fiat currency, financial innovation, German hyperinflation, housing crisis, implied volatility, intangible asset, Kickstarter, large denomination, manufacturing employment, margin call, market bubble, market fundamentalism, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, Northern Rock, Ponzi scheme, price stability, private sector deleveraging, purchasing power parity, pushing on a string, quantitative easing, refrigerator car, reserve currency, short selling, sovereign wealth fund, too big to fail, transaction costs, universal basic income, value at risk, yield curve

In mid-June, 10-year Treasury yields hit 5.3 percent (the highest point since 2002), and in mid-July, the 90-day T-bill rate hit 5 percent, meaning the yield curve was very flat. That was the cyclical peak because of what came next. As interest rates rise, so do debt service payments (both on new items bought on credit, as well as on previously acquired credit that was financed with variable rate debt). This discourages additional borrowing (as credit becomes more expensive) and reduces disposable income (as more money is spent on debt service). Because people borrow less and have less money left over to spend, spending slows, and since one person’s spending is another person’s income, incomes drop, and so on and so forth. When people spend less, prices go down, and economic activity decreases. Simultaneously, as short-term interest rates rise and the yield curve flattens or inverts, liquidity declines, and the return on holding short duration assets (such as cash) increases as their yields rise.

short rate: Interest rates on lending for very short periods of time, usually 3 months or less. stimulation: See “easing.” tightening: Policy moves that reduce the availability of money and credit, which has the effect of slowing economic growth, usually by increasing interest rates, allowing money supplies to shrink, cutting government spending, or changing rules to restrict bank lending. yield curve: The difference between shorter-term interest rates and longer-term interest rates. If short rates are above longer-term rates, the yield curve is said to be inverted, meaning short-term interest rates are priced to fall. If short rates are below longer-term rates, short-term interest rates are priced to rise. 48 Debt Crises This section goes through each of the 48 debt crises we examined, so that you can live through them on your own. This case list was generated by us systematically screening for periods of deleveraging across major countries over the last century—focusing on those cases with a real GDP decline of more than 3%—as well as triangulating that list against the work of others like the IMF and prominent academics.

Runs from risky assets to less risky assets pick up, contributing to a broadening of the contraction. Typically, in the early stages of the top, the rise in short rates narrows or eliminates the spread with long rates (i.e., the extra interest rate earned for lending long term rather than short term), lessening the incentive to lend relative to the incentive to hold cash. As a result of the yield curve being flat or inverted (i.e., long-term interest rates are at their lowest relative to short-term interest rates), people are incentivized to move to cash just before the bubble pops, slowing credit growth and causing the previously described dynamic. Early on in the top, some parts of the credit system suffer, but others remain robust, so it isn’t clear that the economy is weakening. So while the central bank is still raising interest rates and tightening credit, the seeds of the recession are being sown.


pages: 300 words: 77,787

Investing Demystified: How to Invest Without Speculation and Sleepless Nights by Lars Kroijer

Andrei Shleifer, asset allocation, asset-backed security, Bernie Madoff, bitcoin, Black Swan, BRICs, Carmen Reinhart, cleantech, compound rate of return, credit crunch, diversification, diversified portfolio, equity premium, estate planning, fixed income, high net worth, implied volatility, index fund, intangible asset, invisible hand, Kenneth Rogoff, market bubble, money market fund, passive investing, pattern recognition, prediction markets, risk tolerance, risk/return, Robert Shiller, Robert Shiller, selection bias, sovereign wealth fund, too big to fail, transaction costs, Vanguard fund, yield curve, zero-coupon bond

That being the case, although this risk can also lead to you making money it is not a risk you get compensated for taking in the form of higher expected returns. 7 By adding other safe bonds to your minimal risk bonds you are also diversifying your interest rate risk away from that of just one currency (you have exposure to a couple of different yield curves), but for the purposes of keeping the portfolio simple I don’t think this diversification is worth the added complexity and currency risk of those bonds. 8 We have left out the large and broad market of financial institution debt. This includes interbank debt, but also various obligations issued by financial institutions. This is less of a transparent market for the rational investor and someone with the broad exposures discussed in this book already have a lot of the same exposures via the existing bond and equity positions in their portfolio. 9 Occasionally the yield curve is inverted (long-term yields are lower than short-term ones). This is when the market is expecting the interest rate to drop in the future. 10 The straight line between minimal risk, point T, and up to the right assumes that the investor can borrow at the same rate as the minimal risk bond.

As a reward for taking the interest rate risk associated with the longer-term bonds they typically yield more than the short-term bonds, as illustrated in Figure 4.1.2 So if you need a product that will not lose money over the next year, pick short-term bonds to match that profile. However, if you – like most people – are after a product that will provide a secure investment further into the future, pick longer-term bonds and accept the attendant interest-rate risk. Figure 4.1 The typical bond yield curve You should therefore consider the time horizon of your portfolio and select the maturity of your minimal risk bonds accordingly. If you are matching needs far in the future (like your retirement spending) there is certainly merit in adding long-term bonds or even inflation-protected bonds (see below) to your portfolio. Long-term bonds compensate investors for interest-rate risks by offering higher yields and you have the further benefit of matching the timing of your assets and needs.

You will have to accept interest rate risk even if you avoid inflation risk by buying inflation-adjusted bonds. 1 For those who don’t think government bonds can default I would encourage you to read This Time is Different: Eight Centuries of Financial Folly by Carmen Reinhart and Kenneth Rogoff (Princeton University Press, 2011). The authors make a mockery of the belief that governments rarely default and that we are somehow now protected from the catastrophic financial events of the past. 2 There are cases where the yield curve is reversed and shorter-term bonds yield more than longer-term ones, but these cases are less frequent. 3 Imagine the scenario where you want to hold one-month government bonds. Tomorrow the bonds are no longer one-month to maturity, but 29 days. Is this ok? How about 2 days hence? How much you are willing for the maturity to deviate from exactly 30 days is up to you, but in reality there is a trading and administrative cost associated with trading bonds.


All About Asset Allocation, Second Edition by Richard Ferri

activist fund / activist shareholder / activist investor, asset allocation, asset-backed security, barriers to entry, Bernie Madoff, buy and hold, capital controls, commoditize, commodity trading advisor, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, equity premium, estate planning, financial independence, fixed income, full employment, high net worth, Home mortgage interest deduction, implied volatility, index fund, intangible asset, Long Term Capital Management, Mason jar, money market fund, mortgage tax deduction, passive income, pattern recognition, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, Sharpe ratio, stocks for the long run, survivorship bias, too big to fail, transaction costs, Vanguard fund, yield curve

Most of the time, a 10-year Treasury note has had a higher yield than the 1-year T-bill. These are periods with a “normal” yield curve, so called because under normal conditions short-term T-bills are expected to yield less than intermediate-term Treasury notes. A “flat” yield curve occurs when the yield on T-bills and T-notes is the same. If T-bills have a higher yield than Treasury notes, this is known as an “inverted” yield curve. There is a school of thought that believes that when the curve is inverted, the economy is slowing and the stock market will likely go down. There appears to be some support for this theory, although CHAPTER 8 152 FIGURE 8-3 Treasury Term Spread, 1-Year T-Bills, and 10-Year Treasury Notes 4.0 Normal yield curve (long-term rates higher than short-term rates) 3.0 Yield difference 2.0 1.0 0.0 Feb. 89 ⫺1.0 Apr. 00 Jan. 06 ⫺2.0 ⫺3.0 Dec-10 Dec-05 Dec-00 Dec-95 Dec-90 Dec-85 Dec-75 Dec-70 Dec-65 Dec-60 Dec-55 ⫺4.0 Dec-80 Inverted yield curve (short-term rates higher than long-term rates) the timing is sketchy at best.

There appears to be some support for this theory, although CHAPTER 8 152 FIGURE 8-3 Treasury Term Spread, 1-Year T-Bills, and 10-Year Treasury Notes 4.0 Normal yield curve (long-term rates higher than short-term rates) 3.0 Yield difference 2.0 1.0 0.0 Feb. 89 ⫺1.0 Apr. 00 Jan. 06 ⫺2.0 ⫺3.0 Dec-10 Dec-05 Dec-00 Dec-95 Dec-90 Dec-85 Dec-75 Dec-70 Dec-65 Dec-60 Dec-55 ⫺4.0 Dec-80 Inverted yield curve (short-term rates higher than long-term rates) the timing is sketchy at best. Sometimes the curve becomes inverted a couple of years before stocks correct during a recession, and sometimes it inverts after the market has already started to pull back. CREDIT RISK Credit risk is illustrated on the vertical axis in Figure 8-1. Bonds that have more credit risk should pay a higher interest rate than bonds with low credit risk. Table 8-1 shows how three different credit-rating agencies categorize bonds by creditworthiness. Investment-grade bonds have an S&P and Fitch rating of BBB or higher and a Moody’s rating of Baa or higher. Direct and indirect obligations of a government agency, such as Treasury bonds and federal agency bonds, have the least credit risk and yield the least.

Wash Sale Rule The IRS regulation that prohibits a taxpayer from claiming a loss on the sale of an investment if that investment or a substantially identical investment is purchased within 30 days before or after the sale. Yankee Dollars/Bonds Debt obligations, such as bonds or certificates of deposit, bearing U.S. dollar denominations and issued in the United States by foreign banks and corporations. Yield Curve A line plotted on a graph that depicts the yields of bonds of varying maturities, from short term to long term. The line, or “curve,” shows the relationship between short-term interest rates and long-term interest rates. Yield-to-Maturity The rate of return an investor would receive if the securities held in his or her portfolio were held until their maturity dates. INDEX A Actively managed funds, 21, 97 Advisors, 8, 14–15, 313–315 Alternative investments, 189–215, 214t collectibles, 211–214, 212f, 213f commodities, 191–206, 193f, 195f, 201f, 202f, 204t hedge funds, 206–211, 208t list of, 214–215 American Depositary Receipts (ADRs), 129 Asset allocation, ix–xi, 41–64, 63f correlation analysis, 47–53, 57–61 fallibility of, 62–64 history of, 41–44 rebalancing, 44–47 risk and return, 53–57 strategies for, xiv–xv two-asset-class model for, 53 (See also specific topics) Asset allocation stress test, 278–284, 281t, 283t Asset classes, 18–19 with low or varying correlation, 95–97 with low-cost availability, 97–98 range of volatility in, 34–35 with real expected return, 94 REITs as, 175–179 (See also Multi-asset-class investing; specific classes) B Backfill bias, 209 Basis points, 77 Bear markets, 276–277, 294–295 Behavioral finance, 271–289 asset allocation stress test, 278–284, 281t, 283t bear markets, 276–277 observations from, 273–275 personal risk tolerance, 275 rebalancing risk, 284–285 risk avoidance, 285–286, 286t risk tolerance questionnaires, 277–278, 287–289 Beta, 116 Bond market, global, 148–149, 163 Bonds, 19, 89, 90, 148–156 corporate, 72–75 credit risk with, 152–155 emerging market, 164–165 forecasting returns, 228–229, 238–240 high-yield corporate bonds, 157–159 investment-grade, 152–157 maturity structure, 151–152 tax-exempt municipal, 148, 165–166 U.S., 88–89 “your age in bonds,” 243–244, 266–270, 295–296 (See also specific bond types) Bulletin-board stocks, 104, 104t C Canadian stocks, 138–139 Cash/cash-type investments, 10 Changing allocation, 291–299 guidelines for, 298–299 just before retirement, 294–295 and periodic market data, 296–297, 297f reasons for, 292 when goals are within reach, 293–294, 293f when investing for others, 295–296 Collectibles, 211–214, 212f, 213f Commercial real estate investments, 173–175 331 332 Commodities, 30, 189–195, 193f, 195f, 200–206, 201f, 202f, 204t, 224 indexes for, 194–195, 200–201 in portfolios, 201–203 real return on, 94 and supply and demand, 192–194 Commodity funds, 89 Commodity futures, 196–201, 197f, 200f, 203 Computer simulations, 81–82 Corporate bonds, 72–75, 151, 157–159, 229t, 230f Correlation, 47–53, 51t inconsistency of, 57–61, 95 low or varying, 95–97 measuring, 50 negative and positive, 48, 95 for real estate investments, 179–183 with U.S. stocks and bonds, 89 in well-diversified portfolios, 62 Costs of investing (see Fees and costs) Credit risk, 152–155, 153t, 154f, 155f Currency risk, 128–129, 128f, 133t D Default risk (bonds), 158–159 Deflation, 239 Developed markets, 130, 163 Developed-market indexes, 132–134 Diversification, 41, 43f, 55f, 59f, 60f, 60t, 62, 90 within funds, 97 with microcap stocks, 110, 113 rebalancing for, 45 with small-cap value stocks, 121–125 (See also Multi-asset-class investing) Dividends, 235–238 Dollar cost averaging, 309–311 E EAFE Index, 132–138, 133t, 134f, 136f, 138t, 140f Early savers, 244, 247–251, 250f, 250t, 251t Economic factor forecasting, 233–235 Efficient frontier, 54, 54f, 58–59, 66, 123, 124 Index Efficient market theory (EMT), 43 Emerging markets, 98–99, 130–131, 139–141, 140f, 140t, 141f, 163 Equity REITs, 176–183 Exchange-traded funds (ETFs), 311–313 of alternative assets, 214 capital gains on, 305–306 costs and fees with, 303, 304 for global diversification, 99 international, 144 in investment plan, 11–13, 21–22 low cost of, 97 Expectations for returns, 219 (See also Forecasting) F Factor performance analysis: in forecasting, 233–235 international equities, 142–143 U.S. equities, 109–121 Fad investing, 13–14 Fear of regret, 274–275 Federal Reserve, 235 Fees and costs, 301–315, 302t comparing fund expenses, 303–305 cost of taxation, 305–308 index funds and ETFs, 304f, 311–313 low-fee advisors, 313–315 and performance, 302–303 and tax swaps, 308–311 Fixed-income investments, 147–169, 150f, 156t, 167f, 167t, 168t, 223f bond market structure, 148–149 corporate bonds, 72–75 credit risk, 152–155 example of, 166–167 forecasting returns, 238–240 foreign market debt, 163–165 high-yield corporate bonds, 157–159 investment-grade bonds, 155–157 list of, 167–168 maturity structure, 151–152 risk and return with, 149–151 tax-exempt municipal bonds, 165–166 TIPS, 28, 29, 159–163 (See also specific investments) Index Forecasting, 13, 219–242, 234f, 236f, 241t creating forecasts, 240–241 and dividends, 235–238 economic factor, 233–235 Federal Reserve and GDP growth, 235 fixed income returns, 238–240 and inflation, 225–226 market returns, 220–221 risk-adjusted returns, 221–225 stacking risk premiums, 226–232 Foreign market debt, 163–165 Foreign stocks (see International equity investments) Frontier markets, 132 Fund expenses, 303–305 Fundamental differences, 90–93 G Global markets, 98–99, 98f, 129–132, 131f, 135f, 137f Government bonds, 151, 164f, 165f (See also Treasury bonds) Gross domestic product (GDP), 234f, 235 Growth stocks, 108, 114–116, 119–121 H Hedge funds, 190–191, 206–211, 208t High-yield corporate bonds, 157–159, 158f Home ownership, 173, 183–186, 259 I Index funds, 21–22, 304f, 311–313 commodities, 203–206 costs and fees with, 303–304 for global diversification, 99 low cost of, 97 U.S. equity, 125–126 value vs. growth, 92–93 Indexes, 106, 116–121, 118t, 119f, 120f bond, 155–157, 163–164 collectibles, 212–214 commodities, 194–195, 200–201 developed-market, 132–134 EAFE, 132–138 333 emerging market, 139–141 hedge fund, 209–210 international, 68–70 microcap, 111–113 midcap, 111 REIT, 177–178, 180–182 U.S. equities, 105 Inflation, 103, 221 and forecasting, 225–226 and interest rates, 238–240, 239f and real expected return, 94 and rental properties, 173 Inflation-protected securities, 28, 29, 162–163 [See also Treasury Inflation-Protected Securities (TIPS)] International equity investments, 127–145, 142t, 144t allocation of, 137–138, 143 Canadian stocks, 138–139 currency risk, 128–129, 128f, 133t developed-market indexes, 132–134 EAFE Index, 132–138, 133t, 134f, 136f, 138t, 140f emerging markets, 139–141, 140f, 140t, 141f global markets, 129–132, 131f, 135f, 137f list of, 143–144 in multi-asset-class investing, 68–72 size and value factors, 142–143 Investment plan, 3–23 academics’ views of, 19–20 asset allocation in, 15–16 asset classes in, 18–19 avoiding bad advice, 14–15 characteristics of, 4–6 and fad investing, 13–14 monitoring and adjusting, 16–18 mutual funds and ETFs in, 11–13 and overanalysis of market data, 22 and professional advice, 8 selection of investments, 21–22 and shortcuts, 6–7 types of assets in, 9–11 Investment policy statement (IPS), xiii, 5 334 Investment pyramid, 9–11, 9f, 245–247, 246f Investment risk, 25–39 defining, 29–31 and myth of risk-free investments, 26–29 as running out of money in retirement, 31–32 volatility as, 32–38 Investment styles, 19 Investment-grade bonds, 152–157 L Large-cap stocks, 107–109, 117t, 119 Large-cap style indexes, 117 Liability matching, 253–254 Life-cycle investing, 17–18, 243–270 early savers, 247–251, 250f, 250t, 251t investment pyramid in, 245–247, 246f and life phases, 244–245 mature retirees, 263–266, 265f, 265t, 266t midlife accumulators, 252–256, 255f, 255t, 256t modified “your age in bonds” for, 266–270 transitional retirees, 256–262, 261f, 262t Limited partnerships (real estate), 174 Long-term investments, 10 Low-cost asset classes, 97–98 Low-fee advisors, 313–315 M Market data, 22, 296–297 Market returns, forecasting, 220–221 Market risk factor, 116, 272 Markets: bear, 276–277, 294–295 bond, 148–149 developed-market indexes, 132–134 and dividends, 235–238 emerging, 139–141 foreign market debt, 163–165 global, 98–99, 129–132 Index overanalysis of market data, 22 periodic market data, 296–297 stock, 103–105 (See also specific markets) Markowitz, Harry, 41–43 Mature retirees, 244–245, 263–266, 265f, 265t, 266t Microcap stocks, 107–113, 110t, 111f, 124f Midcap stocks, 107–109, 111–113, 111f Midlife accumulators, 244, 252–256, 255f, 255t, 256t Modern portfolio theory (MPT), viii, 43–44, 79, 171, 189, 271 Morningstar classifications, 106–109, 107f, 108t, 109f Morningstar ratings, 14 Multi-asset-class investing, 65–83, 67f corporate bonds, 72–75, 73t, 74f example of, 75–79, 76f, 76t, 78–79f for expanding the envelope, 66–67 international stocks, 68–72, 69t, 70f, 71f Municipal bonds, tax-exempt, 148, 165–166 Mutual funds, 30, 92f, 93f, 148 of alternative assets, 214 capital gains on, 305–306 commodities, 203–206 costs and fees with, 303–305 emerging market, 131 global equity, 130 high-yield bonds, 159 international, 144 in investment plan, 11–13 in late 1990s, 92–93 low-cost fixed-income, 167–168 no-load actively managed, 97 REIT, 174, 175, 187 swapping, 308–309 (See also Index funds) N Nasdaq, 103, 104, 104t New York Stock Exchange (NYSE), 103, 104, 104t No-load actively managed funds, 97 Index Noncorrelation, 48–53, 51f Northwest quadrant, 54, 55f, 66, 80 P Passive funds, 21 Pension plans, 30, 258–259 Performance: factor performance analysis, 109–121 and fees, 302–303 and future results, 14 and investment cost, 302–303 long-term, 16 (See also Forecasting; Returns) Portfolio building (see Investment plan; Life-cycle investing) Portfolio risk, 26, 275 Price-to-earnings (P/E) ratio, 236–238, 237f Pricing bias, 209 Primary market, 103 Professional advisor(s), 8, 14–15, 313–315 R Real estate investment trusts (REITs), 174–182f, 186–187, 187t Real estate investments, 171–187, 172t commercial, 173–175 correlation analysis, 179–183 home ownership, 183–186, 259 list of, 186–187 REITs, 174–182f, 186–187, 187t Real return, 161 on commodities, 94 on U.S. stocks and bonds, 102–103 Rebalancing, 44–47, 46t, 55, 59, 67, 284–285 Regression to the mean, 45 Retirement: bear markets just before, 294–295 and life-cycle investing, 256–266 running out of money in, 31–32 Returns, 35–38, 35t, 56t, 222t and asset allocation, 20 expectations for (see Forecasting) fixed-income, 149–151, 238–240 on international investments, 68–70 335 market, 220–221 with multi-asset-class investing, 75–77 real, 94, 161 on real estate investments, 171–173 on REITs, 180–183 and risk, 35, 53–57, 61f, 223f risk-adjusted, 221–225, 221t on U.S. equity investments, 102–103, 102t Risk: credit, 152–155, 153t, 154f, 155f currency, 128–129, 128f, 133t default, 158–159 with fixed-income investments, 149–151 investment, 25–39 perceived, 26 rebalancing, 284–285 with REITs, 180–183 and return, 35, 53–57, 61f, 221–225, 223f with small-cap value stocks, 121–125 volatility as, 32–38 Risk avoidance, 285–286, 286t Risk diversification, 90, 121–125 Risk premiums, stacking, 226–232, 232t Risk tolerance, 17, 275 Risk tolerance questionnaires, 16–17, 277–278, 287–289 Risk-adjusted returns, 221–225, 221t Risk-free investments, myth of, 26–29 Rolling correlations, 58f, 96, 96f S Secondary market, 103 Selecting investments, 21–22, 87–100 four-step process for, 88 with fundamental differences, 90–93 in global markets, 98–99 guidelines for, 89–98 with low or varying correlation, 95–97 with low-cost availability, 97–98 with real expected return, 94 U.S. stocks and bonds, 88–89 Index 336 Selection bias, 209 Size factor: international equity investments, 142–143 U.S. equity investments, 106, 107 Size risk factor, 116 Small-cap stocks, 107–109, 118t, 121–125, 122t, 123f, 124f, 230–231, 231f Small-cap style indexes, 117–118 Social Security, 10, 11, 258–259 Speculative capital, 11 Stacking risk premiums, 226–232, 232t Standard deviation, 33–38, 34f, 37t, 38t Stock markets, 105 1987 crash, 30–31 in 1990s, 276 in 2007–2009, 276–277 during crises, 89 Stocks, 19, 89–90, 229–230 Canadian, 138–139 international, 68–72 (See also International equity investments) small-cap value, 121–125 U.S., 88–89 (See also U.S. equity investments) Style factor, 107–109 Survivorship bias, 209 T Tax swaps, 308–311, 309f, 310t Tax-deferred accounts, 306–307 Taxes, 19 and after-inflation returns, 225–226 on bonds, 165–166 on commodity funds, 205–206 as investment expense, 305–308 on T-bill returns, 27–28 Tax-exempt municipal bonds, 148, 165–166 Total risk, xi–xii, 43f Transitional retirees, 244, 256–262, 261f, 262t Treasury bills (T-bills), 26–28, 27f, 28f, 151–152, 152f, 225–227, 226f Treasury bonds, 72–75, 151–152, 160–163, 161f, 229t Treasury iBonds, 162–163 Treasury Inflation-Protected Securities (TIPS), 28, 29, 156, 159–163, 161f, 162f, 227–228, 228f, 240 Treasury notes, 152f Two-asset-class model, 53 U Unit investment trusts (IUTs), 97 U.S. bond investments, 88–89 U.S. equity investments, 101–126, 125t, 140f, 141f, 230f and broad stock market, 105 and currency risk, 128–129 factor performance analysis, 109–121 history of returns on, 102–103 list of, 125–126 and market structure, 103–104 Morningstar classification methods, 106–109 selecting, 88–89 sizes and styles of, 106–109 small-cap value and risk diversification, 121–125 V Value risk factor, 116, 142–143 Value stocks, 108, 114–116, 119–125, 231f Volatility, 222, 224, 225f of commodity prices, 94 of foreign stocks, 127–128 of international stocks, 71, 72 as investment risk, 29, 32–38 measuring, 32–34, 33f, 34f price, 29–30 Y Yield spread, 74 “Your age in bonds” approach, 243–244, 266–270, 295–296


pages: 566 words: 155,428

After the Music Stopped: The Financial Crisis, the Response, and the Work Ahead by Alan S. Blinder

"Robert Solow", Affordable Care Act / Obamacare, asset-backed security, bank run, banking crisis, banks create money, break the buck, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, conceptual framework, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, Detroit bankruptcy, diversification, double entry bookkeeping, eurozone crisis, facts on the ground, financial innovation, fixed income, friendly fire, full employment, hiring and firing, housing crisis, Hyman Minsky, illegal immigration, inflation targeting, interest rate swap, Isaac Newton, Kenneth Rogoff, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, market bubble, market clearing, market fundamentalism, McMansion, money market fund, moral hazard, naked short selling, new economy, Nick Leeson, Northern Rock, Occupy movement, offshore financial centre, price mechanism, quantitative easing, Ralph Waldo Emerson, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, short selling, South Sea Bubble, statistical model, the payments system, time value of money, too big to fail, working-age population, yield curve, Yogi Berra

The Fed’s hawks went along kicking and screaming (internally). The dire outlook was overwhelming their usual zeal for tighter money. The good news was that Bernanke and the FOMC doves were firmly in control. The bad news was that the Fed was nearly out of bullets. Eyes would now turn to fiscal policy. THE EXPECTATIONS THEORY OF THE YIELD CURVE The idea that intermediate-and long-term interest rates depend on beliefs (or expectations) about what overnight interest rates (like the federal funds rate) will be in the future is called the expectations theory of the yield curve. It is the basis for Federal Reserve policies that make implicit or explicit commitments about future interest rates. Here’s a simple example: If one-day money costs 2 percent (annualized) today, and the market expects one-day money to cost 3 percent tomorrow, how much should two-day money cost today?

There were three main candidates: The first was more conventional open-market policy. While the federal funds rate was already down to a superlow 1 percent, the FOMC could lower it still further. The markets thought a 50-basis-point cut most likely. Second, the Committee could try to reduce longer-term interest rates by committing to holding its overnight rate low for a long time. Some called that “open-mouth policy.” The idea is based on the expectations theory of the yield curve, which is explained in the accompanying box. Third, the Fed could keep on expanding its balance sheet, which had already soared from $924 billion the week before Lehman to $2,262 billion on December 11. Which option would the Fed choose? It turned out to be all of the above. In the FOMC’s own language, it decided to use “all available tools” to fight the recession. Of course, Bernanke was inventing tools as he went along.

See European Central Bank (ECB) euro, problem of, 418–19, 425–26 financial leadership countries, 417–19 Greece as problem, 380–83, 413–18 as sovereign crisis, 169, 409, 412, 419, 425–26 U.S. economy, impact on, 381–83, 409 European Stability Mechanism, 426 Evans, Charles, 383–84 Excess reserves, reducing rate on, 246–47, 386 Exchange Stabilization Fund (ESF), 146, 180 Exchange-traded derivatives, 61, 281, 436 Executive compensation, 81–84 AIG bonuses after bailout, 137–38, 297 Dodd-Frank provisions, 309 golden parachute to O’Neal (Stan), 152 regulatory efforts, rejection of, 83–84 regulatory needs for, 283–85, 297, 437 risk-based rewards, 81–83, 284 TARP restrictions, 183, 188–91, 202 Exit strategy of Fed, 367–79 European crisis and delay of, 381–83, 409 excess reserves, dealing with, 369–72, 378, 431 and inflation level, 374–75, 378 interest rates, normalizing, 372–74, 378, 431 timing of, 374–75, 378–79 unconventional policy, continuation of, 384–86 Expectations theory of yield curve, 221–23 Fannie Mae/Freddie Mac, 115–19 competitive edge of, 116 conservatorship, 118–19, 287n federal safety net for, 115–16, 118 financial crisis, role in, 117–18 functions of, 115, 324 future view for, 287–88, 297–98, 309 mortgage default, low rate, 72 QE1 asset-purchase, 206–7, 251 in shadow banking system, 60 and subprime mortgages, 71–72, 116–17 vulnerabilities of (2007), 116–17 Farkas, Lee, 355 Federal budget deficit, 387–408 and Bush administration actions, 388–91, 394 and creditworthiness of U.S., 395–96, 400, 401 economic danger of, 395 future view for, 400–408, 430 growth in dollars, 387–88, 392–93 and health care costs, 390, 398, 404, 406 national debt ceiling, raising, 400 Obama attempts to address, 396–400 public opinion of, 393–95 and recovery programs, 234–36, 350–51, 359–61, 392–93 Simpson-Bowles plan, 397, 401–2, 405–8, 430 Federal Deposit Insurance Act (1991), 162 Federal Deposit Insurance Corporation (FDIC) history of, 144, 146–47 marketable debt guarantee, 161–62 money market funds, not insured, 144 receivership authority of, 298, 306, 310 regulatory failure of, 58 systemic risk exception invoked, 162–63 Temporary Liquidity Guarantee Program (TLGP), 161–62, 208, 242 Federal Deposit Insurance Corporation Improvement Act, 306 Federal Housing Finance Agency (FHFA), 118 home price index, 17–18, 18n, 34 Federal Open Market Committee (FOMC) communication problems of, 373–74 funds rate cuts (2007), 96–97, 172 funds rate cuts (2008), 221–23, 244 growth versus exit actions (2011), 381–85 initial response to crisis, 91–93, 95, 171 landmark meeting (2008), 221–23 Operation Twist, 383–84 Federal Reserve anti-Fed sentiments, 276–77, 293–94, 348–49, 352–53, 358–59 bailouts, 105–19, 136–40 balance sheet (2007–2011), 368–72, 431 and bond bubble burst, 44–45 chairman during crisis.


pages: 892 words: 91,000

Valuation: Measuring and Managing the Value of Companies by Tim Koller, McKinsey, Company Inc., Marc Goedhart, David Wessels, Barbara Schwimmer, Franziska Manoury

activist fund / activist shareholder / activist investor, air freight, barriers to entry, Basel III, BRICs, business climate, business cycle, business process, capital asset pricing model, capital controls, Chuck Templeton: OpenTable:, cloud computing, commoditize, compound rate of return, conceptual framework, corporate governance, corporate social responsibility, creative destruction, credit crunch, Credit Default Swap, discounted cash flows, distributed generation, diversified portfolio, energy security, equity premium, fixed income, index fund, intangible asset, iterative process, Long Term Capital Management, market bubble, market friction, Myron Scholes, negative equity, new economy, p-value, performance metric, Ponzi scheme, price anchoring, purchasing power parity, quantitative easing, risk/return, Robert Shiller, Robert Shiller, shareholder value, six sigma, sovereign wealth fund, speech recognition, stocks for the long run, survivorship bias, technology bubble, time value of money, too big to fail, transaction costs, transfer pricing, value at risk, yield curve, zero-coupon bond

In practice, forward rate curves derived from the yield curve will rarely follow the smooth patterns of Exhibit 34.11. Small irregularities in the current yield curve can lead to large spikes and dents in the forward rate curves, which 776 BANKS EXHIBIT 34.11 Yield Curve and Future Interest Rates Interest rate, % Current yield curve Forward 1-year rates Forward 3-year rates Forward 5-year rates Forward 10-year rates 6 5 4 3 2 1 0 2015 2020 2025 2030 2035 2040 would produce large fluctuations in net interest income forecasts. As a practical solution, use the following procedure. First, obtain the forward one-year interest rates from the current yield curve. Then smooth these forward oneyear rates to even out the spikes and dents arising from irregularities in the yield curve. Finally, derive the two-year and longer-maturity forward rates from the smoothed forward one-year interest rates.

Following this theory, it is necessary to ensure that our expectations for interest rates in future years are consistent with the current yield curve. Exhibit 34.11 shows an example of a set of future one-, three-, five-, and ten-year interest rates that are consistent with the yield curve as of 2014. The forecasts for a bank’s interest income and expenses should be based on these forward rates, which constitute the matched-opportunity rates for the different product lines. For example, if the bank’s deposits have a three-year maturity on average, you should use the interest rates from the forward three-year interest rate curve minus an expected spread for the bank to forecast the expected interest rates on deposits in your DCF model. The rates are all derived from the current yield curve. To illustrate, the expected three-year interest rate in 2016 follows from the current three- and six-year yield: [ r2016−2019 ] 13 [ ] 13 (1 + 2.82%)6 = −1 = − 1 = 4.0% (1 + Y2016 )3 (1 + 1.66%)3 (1 + Y2019 )6 where r2016–2019 is the expected three-year interest rate as of 2016, Y2016 is the current three-year interest rate, and Y2019 is the current six-year interest rate.

Suppose general inflation is expected to be 4 percent and unit prices for the company’s products are expected to increase at one percentage point less than general inflation. Overall, the company’s prices would be expected to increase at 3 percent per year. If we assume a 3 percent annual increase in units sold, we would forecast 6.1 percent annual revenue growth (1.03 × 1.03 – 1). 14 U.S. Department of the Treasury, Daily Treasury Yield Curve Rates, November 24, 2014. 254 FORECASTING PERFORMANCE EXHIBIT 11.14 Expected Inflation vs. Growth in the Consumer Price Index % 6 5 4 Implicit expected inflation as derived using 10-year U.S. TIPS bonds 3 2 Annualized growth in the consumer price index 1 0 2000 2002 2004 2006 2008 2010 1 2012 2014 –1 –2 –3 Source: Bloomberg and the Federal Reserve Bank of St. Louis. higher expectations, as the market predicted a stronger recovery than actually occurred.


pages: 318 words: 77,223

The Only Game in Town: Central Banks, Instability, and Avoiding the Next Collapse by Mohamed A. El-Erian

activist fund / activist shareholder / activist investor, Airbnb, balance sheet recession, bank run, barriers to entry, break the buck, Bretton Woods, British Empire, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, carried interest, collapse of Lehman Brothers, corporate governance, currency peg, disruptive innovation, Erik Brynjolfsson, eurozone crisis, financial innovation, Financial Instability Hypothesis, financial intermediation, financial repression, fixed income, Flash crash, forward guidance, friendly fire, full employment, future of work, Hyman Minsky, If something cannot go on forever, it will stop - Herbert Stein's Law, income inequality, inflation targeting, Jeff Bezos, Kenneth Rogoff, Khan Academy, liquidity trap, Martin Wolf, megacity, Mexican peso crisis / tequila crisis, moral hazard, mortgage debt, Norman Mailer, oil shale / tar sands, price stability, principal–agent problem, quantitative easing, risk tolerance, risk-adjusted returns, risk/return, Second Machine Age, secular stagnation, sharing economy, sovereign wealth fund, The Great Moderation, The Wisdom of Crowds, too big to fail, University of East Anglia, yield curve, zero-sum game

El-Erian, “What We Need from the IMF/World Bank Meetings,” Financial Times, October 6, 2013, http://blogs.ft.com/the-a-list/2013/10/06/what-we-need-from-the-imfworld-bank-meetings/. CHAPTER 23: THE BELLY OF THE DISTRIBUTION OF POTENTIAL OUTCOMES 1. “The World in 2015,” Economist, December 2014. 2. Michael J. Casey, “Flattening Yield Curve Latest Complication for Fed,” Wall Street Journal, April 12, 2015, http://blogs.wsj.com/moneybeat/2015/04/12/flattening-yield-curve-latest-complication-for-fed/?mod=WSJ_hps_MIDDLE_Video_Third. 3. Mohamed A. El-Erian, “The Instability in Central Bank Divergence,” Financial Times, February 26, 2014, http://blogs.ft.com/the-a-list/2014/02/26/the-instability-in-central-bank-divergence/. CHAPTER 24: A WORLD OF GREATER DIVERGENCE (I): MULTI-SPEED GROWTH 1.

In the span of a few weeks, the Swiss National Bank would suddenly dismantle a key element of its exchange rate system, and do so in what proved to be an incredibly disruptive manner for markets; Singapore would alter its own exchange rate system; and Denmark would declare that it would refrain from issuing any more government bonds. The next few weeks would also witness a market collapse in government yields, including negative levels all the way out to the nine-year point in the German yield curve and the benchmark ten-year bond there trading at just five basis points (that is, 0.05 percent). They would see investors rush to buy many newly issued bonds directly from some European governments, agreeing to pay (rather than receive) interest income for doing so. And they would witness large banks actively discourage depositors from keeping money with them. These were just some of the many unthinkables.

This discomfort relates to the growing difficulties that both national economies and the global system face (and will face) in reconciling in a relatively stable manner five trends that I believe will become more pronounced in the period ahead—namely: • Multi-speed growth; • Multi-track central banking policies; • Growing pricing anomalies, from negative nominal interest rates to the unusual position of having “the U.S. yield curve…now shaped as much by foreign monetary policy as the Fed’s”;2 • Non-economic headwinds; and • The impact of certain disruptive innovations going macro. Together they suggest that, as opposed to the consensus view of a relatively stable low-growth world, we are looking at increasing economic and policy divergences among countries, which, together with prospects for national political and geopolitical disruptions, will make the belly of the distribution a lot less stable.


pages: 261 words: 86,905

How to Speak Money: What the Money People Say--And What It Really Means by John Lanchester

asset allocation, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, blood diamonds, Bretton Woods, BRICs, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collective bargaining, commoditize, creative destruction, credit crunch, Credit Default Swap, crony capitalism, Dava Sobel, David Graeber, disintermediation, double entry bookkeeping, en.wikipedia.org, estate planning, financial innovation, Flash crash, forward guidance, Gini coefficient, global reserve currency, high net worth, High speed trading, hindsight bias, income inequality, inflation targeting, interest rate swap, Isaac Newton, Jaron Lanier, joint-stock company, joint-stock limited liability company, Kodak vs Instagram, liquidity trap, London Interbank Offered Rate, London Whale, loss aversion, margin call, McJob, means of production, microcredit, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, negative equity, neoliberal agenda, New Urbanism, Nick Leeson, Nikolai Kondratiev, Nixon shock, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, plutocrats, Plutocrats, Ponzi scheme, purchasing power parity, pushing on a string, quantitative easing, random walk, rent-seeking, reserve currency, Richard Feynman, Right to Buy, road to serfdom, Ronald Reagan, Satoshi Nakamoto, security theater, shareholder value, Silicon Valley, six sigma, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, sovereign wealth fund, Steve Jobs, survivorship bias, The Chicago School, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, trickle-down economics, Washington Consensus, wealth creators, working poor, yield curve

The inverse correlation of bond prices and yields is one of those principles that is difficult to get your head around, and I find myself reexplaining it to myself almost every time I come across it. From the point of view of listening to the news, the thing to remember is that yields going up means the debt is being seen as more risky. yield curve The yield projected into the future. If you lend money, the general rule is that longer you’re lending it for, the more your money is at risk. This means that longer loans should offer a higher yield: more risk means the yield has to be more tempting to get you to lend money. The graph of time plotted against risk is called the yield curve, and over time, it goes up, as the risk and yield go up. Sometimes, though, when things are weird and the economy is hitting hard times, investors think that the long-term rates currently on offer are better than the ones they’ll be getting in a few months’ time.

I know all about this type of semiknowledge, because I was completely that person, the one who sorta-kinda knew what was being talked about, but not in enough detail to really engage with the argument in a fully informed, adult manner. Now that I know more about it, I think everybody else should too. Just as C. P. Snow said, in the late 1950s, that everyone should know the second law of thermodynamics,* everyone should know about interest rates, and why they matter, and also what monetarism is, and what GDP is, and what an inverted yield curve is, and why it’s scary. From that starting point, of language, we begin to have the tools to make up an economic picture, or pictures. That’s what I want this book to do: to give the reader tools, and my hope is that after reading it you’ll be able to listen to the economic news, or read the money pages, or the Wall Street Journal, and know what’s being talked about and, just as importantly, have a sense of whether you agree or not.

Sometimes, though, when things are weird and the economy is hitting hard times, investors think that the long-term rates currently on offer are better than the ones they’ll be getting in a few months’ time. They pile into long-term debt, taking the opportunity to get these good rates while they’re still available. The price of those long-term debts goes up. Because price and yield are inversely correlated, the rising price makes the yield on those debts go down: that can mean that the longer-term debt ends up with a lower yield than short-term debt. This is known as an inverted yield curve, and it is a sure sign that the market thinks there is severe trouble just ahead. yuan and renminbi Observers of China refer to both the renminbi and the yuan in talking about the country’s currency. They’re the same thing: renminbi means “the people’s currency,” and it was the name given the new currency at the foundation of the People’s Republic of China in 1949. Yuan means “dollar” and is the unit of the currency; so renminbi is like sterling and yuan is like pound.


pages: 407 words: 114,478

The Four Pillars of Investing: Lessons for Building a Winning Portfolio by William J. Bernstein

asset allocation, Bretton Woods, British Empire, business cycle, butter production in bangladesh, buy and hold, buy low sell high, carried interest, corporate governance, cuban missile crisis, Daniel Kahneman / Amos Tversky, Dava Sobel, diversification, diversified portfolio, Edmond Halley, equity premium, estate planning, Eugene Fama: efficient market hypothesis, financial independence, financial innovation, fixed income, George Santayana, German hyperinflation, high net worth, hindsight bias, Hyman Minsky, index fund, invention of the telegraph, Isaac Newton, John Harrison: Longitude, Long Term Capital Management, loss aversion, market bubble, mental accounting, money market fund, mortgage debt, new economy, pattern recognition, Paul Samuelson, quantitative easing, railway mania, random walk, Richard Thaler, risk tolerance, risk/return, Robert Shiller, Robert Shiller, South Sea Bubble, stocks for the long run, stocks for the long term, survivorship bias, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, the rule of 72, transaction costs, Vanguard fund, yield curve, zero-sum game

On the other hand, the excess return earned by extending bond maturities is minimal, as shown by the “yield curve” for the U.S. Treasury market I’ve plotted in Figure 13-2. Notice that you get about 4% of extra return by extending your maturity from 30 days out to 30 years. This is about as “steep” as the yield curve gets. Much of the time, the curve is much less steep—perhaps 1% to 1.5% difference between long and short yields—and there are even times when the yield curve is “inverted,” i.e., when long rates are lower than shorter rates. Table 13-4. Bond and Bond Index Funds Figure 13-2. U.S. Treasury yield curve. (Source: The Wall Street Journal, 3/14/02.) In Figure 13-2, note that you get the most “bang for the buck” by about a five-year maturity. This is the steepest part of the yield curve—the part that rewards you the most. Beyond that, the extra return diminishes, with continually increasing risk.


Trading Risk: Enhanced Profitability Through Risk Control by Kenneth L. Grant

backtesting, business cycle, buy and hold, commodity trading advisor, correlation coefficient, correlation does not imply causation, delta neutral, diversification, diversified portfolio, fixed income, frictionless, frictionless market, George Santayana, implied volatility, interest rate swap, invisible hand, Isaac Newton, John Meriwether, Long Term Capital Management, market design, Myron Scholes, performance metric, price mechanism, price stability, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, the scientific method, The Wealth of Nations by Adam Smith, transaction costs, two-sided market, value at risk, volatility arbitrage, yield curve, zero-coupon bond

In the case of the former, a prudent risk manager might want to know what happens to this portfolio if interest rates continue to move in nonintuitive ways that are adverse to the portfolio, and he or she might design a set of scenarios to cover the following such contingencies: 1. Relative Value Fixed Income. • Interest rates change by the same magnitude across the yield curve (called a parallel shift). • Rates at earlier maturities increase at a faster rate than those at further out maturities (flattening twist), or vice versa (steepening twist). • Rates at both ends of the yield curve remain relatively constant, while those in between these endpoints either increase or decrease. By using scenario analysis, one can determine the exposures associated with these types of subtle price movements with a great deal more precision than by using other methods of exposure estimation.

I heartily recommend that you calculate and compare these averages across different segments of your portfolio, over different time horizons, and so on. For example, you may want to calculate your average return on longs versus shorts in order to determine whether there is a discernible bias in your market orientation. Similarly, you can compare your averages across market sectors (for equities), underlying markets (for futures), segments of the yield curve (for fixed income trading), and so on. Look carefully here for differences in your unit performance. Are they tied to external factors such as market conditions or perhaps to areas of expertise or trading comfort? By calculating averages across these factors, you can begin to form an idea of what is working in your portfolio and what isn’t. Once you have mastered this relatively simple concept, you may wish to manipulate the calculation in such a way as to provide additional Understanding the Profit/Loss Patterns over Time 57 insights akin to those just described.

By using scenario analysis, one can determine the exposures associated with these types of subtle price movements with a great deal more precision than by using other methods of exposure estimation. In the case of relative value plays involving instruments of differing credit quality, the designers of scenario analysis routines will typically try to estimate the impact of changes in credit spreads, defined as the premium that lenders will demand of borrowers of lesser credit quality. Like the yield curve manipulations mentioned immediately earlier, these exercises are likely to capture risks that are assumed away by the aggregations embedded in the VaR calculation. 2. Convertible Bond Arbitrage. The typical configuration of a convertible bond arbitrage portfolio is one under which the portfolio contains inventories of bonds that are convertible into stock, as hedged by short positions in the cash equity securities of the same corporations. Because these bonds are typically highly correlated to their associated stocks, most VaR programs would fail to register much exposure.


High-Frequency Trading by David Easley, Marcos López de Prado, Maureen O'Hara

algorithmic trading, asset allocation, backtesting, Brownian motion, capital asset pricing model, computer vision, continuous double auction, dark matter, discrete time, finite state, fixed income, Flash crash, High speed trading, index arbitrage, information asymmetry, interest rate swap, latency arbitrage, margin call, market design, market fragmentation, market fundamentalism, market microstructure, martingale, natural language processing, offshore financial centre, pattern recognition, price discovery process, price discrimination, price stability, quantitative trading / quantitative finance, random walk, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, Tobin tax, transaction costs, two-sided market, yield curve

Another is the simplicity of the products themselves: for trading a single name of stock you need very little information beyond its price. Relationships between different stocks are at best weak. As a consequence, quant researchers in equity markets have focused intensively on the details of the execution process. By contrast, fixed-income products are inherently complex, and quantitatively minded researchers in this area have focused on such aspects as yield curve modelling and day counts. Asset managers have not traditionally focused on measuring or managing execution costs, and have few effective tools to do so. However, the Securities Industry and Financial Markets Association (SIFMA) noted that “It is clear that the duty to seek best execution imposed on an asset manager is the same regardless of whether the manager is undertaking equity or fixed-income transactions” (SIFMAAsset Management Group 2008). 43 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 44 — #64 i i HIGH-FREQUENCY TRADING This chapter discusses some details of the fixed-income markets that present special challenges for best execution in general and automated trading in particular.

. • Information events: interest rates markets are strongly af- fected by events such as economic information releases and government auctions. In contrast to earnings releases in the equities markets, these events generally happen in the middle of the trading day and we must have a strategy for trading through them. • Cointegration: interest rates products generally differ only in their position on the yield curve. Thus, they move together to a much greater degree than any collection of equities. To achieve efficient execution in a single product, we must monitor some subset of the entire universe of products. • Pro rata matching: because futures products are commonly traded on a single exchange (in contrast to the fragmentation in the equities markets), the microstructural rules of trading can be much more complex.

That is, it identifies the historical relationship between the two products on an intra-day timescale, and supposes that, when they deviate from this relationship, future prices will evolve so as to restore the relationship. A systematic test of the accuracy of the cointegration prediction shows that it is far less than perfectly accurate, but still effective enough to add value to real-time trading. Figure 3.8 shows the principal components for the full set of four price series shown in Figure 3.4. This corresponds to what would be obtained by a traditional analysis of yield curve dynamics, but here on an intra-day timescale. The first component represents the overall market motion, while the other components represent shifts 57 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 58 — #78 i i HIGH-FREQUENCY TRADING Figure 3.7 Short-term price predictor, using the projections shown in Figures 3.5 and 3.6 A B C D 04.00 06.00 E F G H I ZBH3 ZNH3 00.00 02.00 08.00 10.00 12.00 14.00 16.00 CST on Tuesday December 11, 2012 Black lines are the raw price series as in Figure 3.4.


pages: 293 words: 88,490

The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction by Richard Bookstaber

"Robert Solow", asset allocation, bank run, bitcoin, business cycle, butterfly effect, buy and hold, capital asset pricing model, cellular automata, collateralized debt obligation, conceptual framework, constrained optimization, Craig Reynolds: boids flock, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, dark matter, disintermediation, Edward Lorenz: Chaos theory, epigenetics, feminist movement, financial innovation, fixed income, Flash crash, Henri Poincaré, information asymmetry, invisible hand, Isaac Newton, John Conway, John Meriwether, John von Neumann, Joseph Schumpeter, Long Term Capital Management, margin call, market clearing, market microstructure, money market fund, Paul Samuelson, Pierre-Simon Laplace, Piper Alpha, Ponzi scheme, quantitative trading / quantitative finance, railway mania, Ralph Waldo Emerson, Richard Feynman, risk/return, Saturday Night Live, self-driving car, sovereign wealth fund, the map is not the territory, The Predators' Ball, the scientific method, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, tulip mania, Turing machine, Turing test, yield curve

I have seen this issue repeatedly in risk management, and it is one reason any risk management model will not cover all risks. Once the model is specified, the traders will try to find a way around it. Are you measuring interest rate risk? Well, fine, then I will do a trade that is interest rate neutral but bets on the slope of the yield curve. Now you’re measuring yield curve risk? Fine, then I will do a trade that is both interest rate and yield curve neutral, but rests on the curvature of the yield curve—a butterfly trade. And as this game is being played, the complexity and thus endogenous risk is increasing with each iteration. One of the problems with the standard risk measures is that they become exposed to multiple dimensions for such gaming, and for gaming in a way that is harder to detect. In fact, it was largely reliance on these measures (such as value at risk) that allowed for the ballooning of risks in the banks pre-2008.


pages: 246 words: 16,997

Financial Modelling in Python by Shayne Fletcher, Christopher Gardner

Brownian motion, discrete time, interest rate derivative, London Interbank Offered Rate, stochastic volatility, yield curve, zero day, zero-coupon bond

+ccy+".hw" mr = env.retrieve constant(key) if mr <> 0: term var *= (math.exp(2.0*mr*t)-1.0)/(2.0*mr) else: term var *= t return math.sqrt(term var) The Hull–White Model 101 def local vol(self, t, T, ccy, env): assert t <= T key = "cv.mr."+ccy+".hw" mr = env.retrieve constant(key) return math.exp(-mr*t)-math.exp(-mr*T) The requestor class uses the class environment implemented in the ppf.market.environment module. The purpose of this class is to provide access to market data objects such as yield curves, volatility surfaces, correlation surfaces, etc. Refer to section 5.3 for the details. The following code snippets illustrate how to construct a requestor and make a request for a discount factor and a term volatility: >>> import math >>> import ppf.market >>> from ppf.math.interpolation import loglinear >>> times = [0.0, 0.5, 1.0, 1.5, 2.0] >>> factors = [math.exp(-0.05*t) for t in times] >>> c = ppf.market.curve(times, factors, loglinear) >>> env = ppf.market.environment() >>> key = "zc.disc.eur" >>> env.add curve(key, c) >>> r = requestor() >>> t = 1.5 >>> print r.discount factor(t, "eur", env) 0.927743486329 >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> 0.1 import math import ppf.market from numpy import zeros expiries = [0.1, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0] tenors = [0, 90] values = zeros((8, 2)) values.fill(0.04) surf = ppf.market.surface(expiries, tenors, values) env = ppf.market.environment() key = "ve.term.eur.hw" env.add surface(key, surf) key = "cv.mr.eur.hw" env.add constant(key, 0.0) r = requestor() t = 0.25 print r.term vol(t, "eur", env) 8.1.2 State When pricing a financia instrument we frequently need to know about the state of the world – the world being both define and modelled by the chosen model.

The explanatory variables are then returned to the client. class cle exercise: def init (self, l): self. leg = l 116 Financial Modelling in Python def num explanatory variables(self): return 2 def call (self, t, fill, state, requestor, env): # harvest active flows all flows = self. leg.flows() flows = [] for flow in all flows: accrual start days = env.relative date( flow.accrual start date()) if accrual start days >= t*365.0: flows.append(flow) if len(flows) < 1: raise RuntimeError, "no active flows remaining" # explanatory variables num sims = state.shape[0] evs = numpy.zeros((num sims, self.num explanatory variables())) pv01 = numpy.zeros(num sims) notl exchange = numpy.zeros(num sims) cnt = 0 for flow in flows: Ts = env.relative date(flow.accrual start date())/365.0 Te = env.relative date(flow.accrual end date())/365.0 Tp = env.relative date(flow.pay date())/365.0 dfp = fill.numeraire rebased bond(t, Tp, flow.pay currency()\ , env, requestor, state) pv01 += flow.year fraction()*dfp if cnt == 0: dfs = fill.numeraire rebased bond(t, Ts, flow.pay currency()\ , env, requestor, state) notl exchange = dfs dfe = fill.numeraire rebased bond(t, Te, flow.pay currency()\ , env, requestor, state) evs[:, 0] = (dfs/dfe-1.0)/flow.year fraction() elif cnt == len(flows)-1: notl exchange -= fill.numeraire rebased bond(t, Te, flow.pay currency(), env, requestor, state) cnt = cnt+1 evs[:, 1] = notl exchange/pv01 return evs Note that the above component is model independent and therefore could be re-used for other models. Unit tests for the exercise component are provided in the module ppf.test.test hull white. The method test explanatory variables on the class exercise tests checks that the computed explanatory variables, the LIBOR and swap rates, The Hull–White Model 117 match the corresponding rates taken from the yield curve for the case when the Hull–White volatilities are all zero. def test explanatory variables(self): from ppf.math.interpolation import loglinear times = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0] factors = [math.exp(-0.05*t) for t in times] c = ppf.market.curve(times, factors, loglinear) expiries = [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0] tenors = [0, 90] values = numpy.zeros((8, 2)) surf = ppf.market.surface(expiries, tenors, values) from ppf.date time \ import date, shift convention, modified following, basis act 360, months pd = date(2008, 01, 01) env = ppf.market.environment(pd) key = "zc.disc.eur" env.add curve(key, c) key = "ve.term.eur.hw" env.add surface(key, surf) key = "cv.mr.eur.hw" env.add constant(key, 0.0) r = ppf.model.hull white.requestor() s = ppf.model.hull white.monte carlo.state(10) sx = s.fill(0.25, r, env) f = ppf.model.hull white.fill(3.0) flows = ppf.core.generate flows( start = date(2008, 01, 01) , end = date(2010, 01, 01) , duration = months , period = 6 , shift method = shift convention.modified following , basis = "ACT/360" , pay currency = "EUR") lg = ppf.core.leg(flows, ppf.core.PAY) ex = ppf.model.hull white.monte carlo.cle exercise(lg) t = env.relative date(flows[1].accrual start date())/365.0 T = env.relative date(flows[1].accrual end date())/365.0 ret = ex(t, f, sx, r, env) dft = c(t) dfT = c(T) expected libor = (dft/dfT-1.0)/flows[1].year fraction() pv01 = 0.0 for fl in flows[1:]: T = env.relative date(fl.pay date())/365.0 dfT = c(T) pv01 += fl.year fraction()*dfT T = env.relative date(flows[-1].accrual end date())/365.0 dfT = c(T) expected swap = (dft-dfT)/pv01 118 Financial Modelling in Python expected libors = numpy.zeros(10) expected libors.fill(expected libor) expected swaps = numpy.zeros(10) expected swaps.fill(expected swap) actual libors = ret[:, 0] actual swaps = ret[:, 1] assert seq close(actual libors, expected libors) assert seq close(actual swaps, expected swaps) 8.2 THE MODEL AND MODEL FACTORIES The model class brings all the components from the preceding sections together into one place.

.; mathematics; NumPy; ppf package basics 193–205 batch interpreter mode 193–4 benefit 1–4 built-in data types 1, 195–7 C++/Python ‘Hybrid Systems’ 4, 159–63 C API routines 19–26, 161–3 C/C++ interoperability benefit 2, 3–4, 7–9, 11–26, 157 class basics 2–3, 201–3 COM servers 4, 5–6, 98, 165–89 concepts 1–4, 193–205 control fl w statements 197–200 dictionaries 119–22, 181, 196–7, 215–16 dynamic type system 2–3 encapsulation support 2–3, 58–61, 209 expressiveness aspects 1 extensibility aspects 1–4, 7–9, 11–26 financia engineering 1–4, 11–26 function basics 2–3, 200–1 functional programming idioms 1 GUI toolkits 2 high-level aspects 1 indented code 198–200 inheritance basics 122, 202–3, 209–11 interactive interpreter mode 193–4 interoperability aspects 1–4, 7–9, 11–26, 157 interpreters 2, 24–6, 45–6, 193–4, 208–9 list basics 3, 196–7, 215 Microsoft Excel 4, 165–89 misconceptions 2–3 module basics 203–5 overview of the book 3–4 package basics 1, 203–5 productivity benefit 1 simple expressions 194–5 Index standard libraries 1 structure misconceptions 2–3 tuples 131, 195–7, 200–1, 215 visualisation software integration 2 whirlwind tour 193–205 white space uses 198–200 Python Distutils package 9 Python Programming on Win32 (Hammond & Robinson) 165 Python Scripting for Computational Science (Langtangen) 2 python.hpp 214–15 python -i command 193 PyUnit testing module, concepts 6–7, 9 quadratic roots, concepts 3, 46–9 quadratic fo 132–42 quadratic roots 46–9 quantitative analysis 1–4, 27–61, 123–43, 165–89 raise 15–16, 30–1, 35–6, 40–1, 43–5, 50–8, 66–7, 78, 81–3, 85–7, 88, 89, 91, 98, 102, 105–6, 113, 118–19, 131, 133, 134–6, 166–9, 170–6 random 27–8, 45–6 random number generation, concepts 3, 27–8, 45–6, 112–22 random variables, expectation calculations 3, 49–61 range function, Python basics 197–8 ratio 48–9 rcv flows 89–91, 96–8, 120–2 redemption cap 153–6 redemption floor 153–6 reference counts 20–6 references, C++ 212–14 reg clsid 169–76, 177–87, 188–9 register com class 169–76 register date... 11–16, 160–3 register date more.cpp 160 register numpy.cpp 23 register special functions 18–19 reg progid 169–76, 177–89 regression schemes 4, 132–42, 150–2, 219–20 regression model 136–42, 150–2 regressions 132–42 regrid 55–61 regridder 58–61 regrid fs 55–7 regrid xT 58–61 regrid yT 58–61 relative date 66–7, 105–22, 125–8, 146–57 233 requestor 100–22, 124–8, 129–42, 146–57 requestor component, pricing models 100–22, 124–8, 129–42 reset currencies, concepts 70–9, 96–8, 105–22 reset dates 69–79, 95–8 see also observables reset basis 72–9, 89–91, 96–8, 120–2, 178–87 reset ccy 69–79, 90 reset currency 71–9, 89–90, 96–8, 105–22, 177–87 reset date 69–79, 95–8 reset duration 73–9, 89–91, 96–8, 120–2 reset holiday centres 72–9 reset id 69–79, 93–8, 146–57 reset lag 72–9 reset period 73–9, 89–91, 96–8, 120–2 reset shift method 72–9, 89–91, 96–8, 120–2, 178–87 retrieve 66–7, 97–8, 124–8, 169–76, 179–87, 188–9 retrieve constant 67, 100–22 retrieve curve 66–7, 100–22, 148–57 retrieve surface 67, 100–22 retrieve symbol... 97–8, 124–8, 131–42, 149–52 return statements, Python basics 200–5 risk the Greeks 142–3 management systems 4 Robinson, Andy 165 rollback 57–61, 108–22, 124–8 rollback component, pricing models 108–22, 124–8, 177–87 rollback max 57–61, 108–22, 124–8 rollback tests 109–22 roll duration 72–9, 84–5, 89–91, 96–8, 120–2, 177–87 roll end 72–9, 81–3, 84–5, 86–91 roll period 72–9, 84–5, 89–91, 96–8, 120–2, 177–87 rolls 14–16, 72–3 roll start 77–8, 81–3, 86–91 root-findin algorithms bisection method 35–6, 37 concepts 3, 35–7 Newton–Raphson method 36–7 root finding 35–7 roots 46–9, 53–7 RuntimeError 30–1, 35–6, 40–1, 43–5, 50–8, 66–7, 77, 81–3, 85–7, 88, 89, 90, 98, 102, 104–5, 113, 118–19, 131, 133, 134–6, 147–8, 169, 170–6, 177–8, 202–3 sausage Monte-Carlo method 143 Schwartz, E.S. 219 234 Index SciPy 1, 3, 8 see also NumPy scope guard techniques 20 SDEs 218 second axis 64–5 seed 112–22, 150–2 self 31–4, 45–6, 51–61, 63–7, 69–79, 93–122, 124–8, 130–42, 146–57, 178–89 semi-analytic conditional expectations, concepts 57–61 semi analytic domain integrator 57–61 server 166–89 set event 126–8, 130–42 set last cfs 135–42 sgn 46–9 shape 43–6, 50–1, 58–61, 81–3, 103–22, 133–42 shared ptr hpp 20–4 shift 14–16, 72–9, 86–8, 111–22 shift convention 14–16, 73–9, 80–2, 83–91, 96–8, 120–2, 151–2 shift method 14–16, 73–9, 80–2, 83–91, 120–2 short rates 101–2 sig 42–6, 65 sign 35–6 simple expressions, Python basics 194–5 sin 205 singular value decomposition of a matrix see also linear algebra concepts 42–6 singular value decomposition back substitution 42–6 solve tridiagonal system 2–3, 34, 39–40 solve upper diagonal system 17–19, 40–4, 50–1 solving linear systems see also linear algebra concepts 39–40 solving tridiagonal systems see also linear algebra concepts 2–3, 34, 39–40 solving upper diagonal systems see also linear algebra concepts 17–19, 40–4, 49–51 sort 48–9 special functions 17–18, 27–61 spread 70–9, 154–6 sqrt 48–9, 52–7, 59–61, 100–22 square tridiagonal matrices 33–4, 40–1 standard deviations 44–6, 51–7, 102–22, 133–42, 188–9 standard libraries 1 standard normal cumulative distributions see also N concepts 3, 27–9, 31, 51–7, 102–22 start 80–3, 83–91, 96–8, 120–2, 177–87, 198–200 start date 83–4 start of to year 16 state 59–61, 102–22, 124–8, 129–42, 146–57 state component, pricing models 101–22, 124–8, 129–42, 145–52 stddev 53–7, 102–22 step, Python basics 198–200 STL functions, C++ 29 stochastic volatility 113–14 stop, Python basics 198–200 str 71–9, 80–3, 86–7, 94, 166–8, 170–6 string literals, Python basics 194–6 structure misconceptions, Python 2–3 sum array 23–6 surf 101–22 surface 64–7, 101–22 surfaces see also environment concepts 3, 63, 64–7, 100–22, 170–6 definitio 64 volatility surfaces 3, 6, 63–7, 100–22 surface tests 64–7 svd 42–4 see also singular value decomposition of a matrix swap rates 70–9, 104–5, 115–22 swap obs 105–22 swap rate 74–9, 116–22 swaps 4, 70–9, 101–2, 104–5, 115–22, 123–8, 132–42, 145–52, 157 swap tests 149–52 swaptions 4, 101–2, 115–16, 126–8, 132–42, 145–52, 157 symbol table 97–8, 124–8, 129–42 symbol table listener 125–8, 129–42 symbol value pair 130–42, 155–6 symbol value pairs to add 130–42, 155–6 sys 27–8 table 82–4, 169 tables, adjuvants 82–4, 147–52, 153–6, 177–87 tag 169–76, 177–89 target redemption notes (TARNs) 4, 101–2, 145, 152–7 concepts 152–7 definitio 152 pricing models 4, 101–2, 145, 152–7 target indicator 153–6 tarn coupon leg payoff 152–6 tarn funding leg payoff 154–6 Index TARNs see target redemption notes tarn tests 155–6 templates 18–26, 159–63 tenor duration 72–9 tenor period 72–9 tenors 67, 84–5, 101–22, 170–6 term 28–9, 103–22 term structure of interest rates see yield curves term volatility, Hull–White model 100–22 terminal T 104–22 term var 100–22 term vol 100–22 test 6–7, 9, 17–19, 59–61, 64–7, 109–22, 148–57 test bond 115–22 test bond option 111–22 test bound 30–1 test bound ci 31 test constant 111–22 test discounted libor rollback 109–22 test explanatory variables 117–22 test hull white 67, 109–22 testing concepts 6–7, 9, 17–19 test lattice pricer 148–57 test market 64–7 test math 59–61 test mean and variance 114–22 test monte carlo pricer 154–6 test value 149–52 theta 48–9, 205 throw error already set 21–6 timeline 94–8, 125–8, 129–42 see also events Tk 2 tline 96–8 to ppf date 168–9, 178–87 tower law 60 tower law test 60–1 trace 23–6 Traceback 195–7, 202–3 trade 87–91, 94–8, 125–8, 129–42, 150–7, 177–87 trade representations, concepts 3, 69–91, 93–8 trade server, COM servers 176–87 trade utilities, concepts 88–91 trade VBA client 181 trade id 188–9 trades see also exercise...; fl ws; legs concepts 3, 69, 87–91, 93–8, 123–43, 176–87 definitio 69, 88 TradeServer 176–87, 188–9 trade server 176–87, 188–9 235 trade utils 89–91, 129–42, 153–6, 176–87 transpose 41–4 tridiagonal systems 2–3, 33–4, 39–40 try 27–8, 171–6, 177–87 Trying 6–7 tuples, Python basics 131, 195–7, 200–1, 215 TypeError 195–7 Ubuntu... 8 underlying 127–8, 130–42 unicode 172 unit fo 132–42 update indicator 134–42 update symbol 97–8, 126–8, 131–42 upper bound 29–31 USD 70–83, 152 utility 6–7, 29–61, 64–7 utility functions 17–26, 29–61 utils 168–76, 187–9 values 101–22 vanilla financia instruments, pricing approaches 99, 123–8, 145–57 var 102–22 variance 51–61, 102–22 variates 103–22 varT 102–22 Vasicek models 217–18 see also Hull–White model VB... see Microsoft... vector 41, 44–6, 133–42, 212 vectorize 133–42 visualisation software integration, Python benefit 2 vol 51–7, 59–61, 114–22 volatility Hull–White model 100–22 piecewise polynomial integration 51–61 surfaces 3, 6, 63–7, 100–22 vols 65 volt 59–61, 59–61 weekdays 15–16, 159–63 while statements, Python basics 199–200 white space, Python basics 198–200 win32 165–89 Win32 Python extensions 165–89 xh 52–7 xl 52–7 xprev 53–7 xs 56–61 xsT 60–1 xT 58–61, 108–22 xtT 58–61, 108–22 xt 58–61, 108–22


pages: 194 words: 59,336

The Simple Path to Wealth: Your Road Map to Financial Independence and a Rich, Free Life by J L Collins

"side hustle", asset allocation, Bernie Madoff, buy and hold, compound rate of return, diversification, financial independence, full employment, German hyperinflation, index fund, money market fund, nuclear winter, passive income, payday loans, risk tolerance, Vanguard fund, yield curve

Generally speaking, short-term bonds pay less interest as they are seen as having less risk since your money is tied up for a shorter period of time. Accordingly, long-term bonds are seen as having higher risk and pay more. If you are a bond analyst, you’ll graph this on a chart and create what is called a yield curve. The chart on the left is fairly typical. The greater the difference between short, mid and long-term rates, the steeper the curve. This difference varies and sometimes things get so wacky short-term rates become higher than long-term rates. The chart for this event produces the wonderfully named Inverted Yield Curve and it sets the hearts of bond analysts all aflutter. You can see what that looks like in the illustration on the right. Stage 7 Inflation is the biggest risk to your bonds. As we’ve discussed, inflation occurs when the cost of goods is rising.

When you lend your money by buying bonds, during periods of inflation when you get it back it will buy less stuff. Your money is worth less. A big factor in determining the interest rate paid on a bond is the anticipated inflation rate. Since some inflation is almost always present in a healthy economy, long-term bonds are sure to be affected. That’s a key reason they typically pay more interest. So, when we get an Inverted Yield Curve and short-term rates are higher than long-term rates, investors are anticipating low inflation or even deflation. Stage 8 Here are a few other risks: Credit downgrades. Remember those rating agencies we discussed above? Maybe you bought a bond from a company rated AAA. This is the risk that sometime after you buy the company gets in trouble and those agencies downgrade its rating. The value of your bond goes down with it.


Triumph of the Optimists: 101 Years of Global Investment Returns by Elroy Dimson, Paul Marsh, Mike Staunton

asset allocation, banking crisis, Berlin Wall, Bretton Woods, British Empire, buy and hold, capital asset pricing model, capital controls, central bank independence, colonial rule, corporate governance, correlation coefficient, cuban missile crisis, discounted cash flows, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, European colonialism, fixed income, floating exchange rates, German hyperinflation, index fund, information asymmetry, joint-stock company, negative equity, new economy, oil shock, passive investing, purchasing power parity, random walk, risk tolerance, risk/return, selection bias, shareholder value, Sharpe ratio, stocks for the long run, survivorship bias, technology bubble, transaction costs, yield curve

At times of inflation uncertainty, short-term bonds become the lower risk investment even for investors with long-term (real) liabilities. We can gain further clues by looking at the yield curve, and at the difference between the redemption yield at which long bonds are trading, and the yield on short-term bills. This data was presented graphically in Figure 6-2, which showed that for the first twenty or so years of the last century, US short-term interest rates were typically above long-term bond yields. Over the eighty years from 1921–2000, however, long bond yields have generally been above short rates by an average of around 1.3 percent per year, with mid-maturity bonds typically lying in between. There has thus normally been an upward sloping yield curve, with yields rising with term to maturity. There can be at least two reasons for an upward sloping yield curve. First, short-term interest rates may be expected to rise.

First, short-term interest rates may be expected to rise. Alternatively, investors may require some form of liquidity or risk premium for holding long bonds to compensate them for uncertainty about the real interest rate, inflation, or both. Since US interest rates in the early 1920s were similar to those at end-2000, it seems most likely that the tendency for the yield curve to have sloped upward over this period is related to some form of risk premium. While we cannot measure investors’ ex ante requirements or expectations relating to this risk premium, we can measure the bond maturity premia actually achieved. The formula for the bond maturity premium is 1 + Long bond rate of return divided by 1 + Treasury bill rate of return, minus 1. The line plot in Figure 6-6 shows the sequence of annual bond maturity premia for the United States from 1900–2000.

., 85 World Bank, 12, 15, 93 World Index, 7, 10, 39–40, 108–14, 119, 123, 166, 167, 168, 171–5, 184–5, 187, 192, 193, 202, 216, 219, 220, 223, 311–5 World ex-US index, 109– 11 World markets, 5, 11–4, 32, 50–1, 123, 138 World Trade Center, see September 11th 2001 World wars, 36, 37, 44, 47, 58, 69, 70, 71, 73, 75, 76, 79, 93, 94, 98, 116, 122, 123, 152, 153, 189, 195, 221, 224 see also First World War and Second World War Wright, S., 195 Wydler, D., xii, 294 Xu, Y., 42, 57, 118 Yield, see bond yield and dividend yield Yield curve, 81 Yugoslavia, 20 Ziemba, W.T., 199, 269 Zingales, L., 22


Investment: A History by Norton Reamer, Jesse Downing

activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, asset allocation, backtesting, banking crisis, Berlin Wall, Bernie Madoff, break the buck, Brownian motion, business cycle, buttonwood tree, buy and hold, California gold rush, capital asset pricing model, Carmen Reinhart, carried interest, colonial rule, credit crunch, Credit Default Swap, Daniel Kahneman / Amos Tversky, debt deflation, discounted cash flows, diversified portfolio, dogs of the Dow, equity premium, estate planning, Eugene Fama: efficient market hypothesis, Fall of the Berlin Wall, family office, Fellow of the Royal Society, financial innovation, fixed income, Gordon Gekko, Henri Poincaré, high net worth, index fund, information asymmetry, interest rate swap, invention of the telegraph, James Hargreaves, James Watt: steam engine, joint-stock company, Kenneth Rogoff, labor-force participation, land tenure, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, margin call, means of production, Menlo Park, merger arbitrage, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, Network effects, new economy, Nick Leeson, Own Your Own Home, Paul Samuelson, pension reform, Ponzi scheme, price mechanism, principal–agent problem, profit maximization, quantitative easing, RAND corporation, random walk, Renaissance Technologies, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, Sand Hill Road, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spinning jenny, statistical arbitrage, survivorship bias, technology bubble, The Wealth of Nations by Adam Smith, time value of money, too big to fail, transaction costs, underbanked, Vanguard fund, working poor, yield curve

Fixed income hedge funds differ fairly widely in the riskiness of the strategies employed. Some are rather risk averse, seeking to buy attractive debt securities that deliver healthy and uninterrupted payments. Others have far more complicated schemes to garner returns, such as exploiting aberrations in yield curves. This can occur when the yield curve adopts an unusual geometry (often a flat or steep slope at the extreme) and managers place long and short positions that profit when the yield curve shifts. One of the most common fixed income strategies is the “swap spread,” which involves collecting the difference between Treasury rates and the swap rate. Others invest in mortgage-backed securities, like those packaged by Fannie Mae and Freddie Mac. Macro hedge funds seek to anticipate major structural changes in an economy, either because of natural market forces (perhaps the market is grossly overheated and is due for a correction) or because of political circumstances.

Quant funds use quantitative factors in models to develop, buy, or sell signals for stocks, commodities, or currencies. These models have a wide spectrum of sophistication. Some of them are simple, trying to capitalize on well-studied sources of risk premium in the stock market like momentum, value, and small market capitalization. Others are more complicated, analyzing convergence-divergence patterns, steepness or flatness of yield curves or futures curves, or even sifting through press releases and conference calls for information on a stock that the market has neglected. The quant fund faces a few difficulties that the best groups are able to overcome. The first is to ensure that the models are not overfitted to historical patterns. In other words, if a strategy is designed or tweaked using historical price behavior, there is a temptation to retrofit a strategy that worked well in the past but may not be capturing the underlying dynamics that would cause it to be successful in the future.

(Rozanov), 128 whole life insurance, 132 Wickham, Richard, 46–47 Wiggin, Albert, 190–91 William and Flora Hewlett Foundation, 127 William III (king of England), 73, 97 William of Orange (stadtholder of Dutch Republic), 86 Williams, John Burr, 4, 232–33 Williams, Ted, 311–12 Wilsonian internationalism, 199 Wisselbank, 85 “World’s Largest Hedge Fund Is a Fraud, The” (Markopolos), 152 World War I: impacts of, 95, 162; inflation during, 198; transition out of, 197–98 436 Investment: A History World War II: bull market after, 92, 143; economy and, 275; impacts of, 96; mutual funds during postwar period, 142–44; price and wage fixing of, 110 Wujinzang (Buddhist temples’ wealth), 29 Wu Zetian, 29 Xenophon, 18 Yale University, 257, 296, 328, 332 yield curves, aberrations in, 266 Zarossi, Luigi, 156 Zhiku lending institution, 29–30


pages: 192 words: 75,440

Getting a Job in Hedge Funds: An Inside Look at How Funds Hire by Adam Zoia, Aaron Finkel

backtesting, barriers to entry, collateralized debt obligation, commodity trading advisor, Credit Default Swap, credit default swaps / collateralized debt obligations, discounted cash flows, family office, fixed income, high net worth, interest rate derivative, interest rate swap, Long Term Capital Management, merger arbitrage, offshore financial centre, random walk, Renaissance Technologies, risk-adjusted returns, rolodex, short selling, side project, statistical arbitrage, stocks for the long run, systematic trading, unpaid internship, value at risk, yield curve, yield management

Fixed Income Arbitrage A fund that follows this strategy aims to profit from price anomalies between related interest rate securities. Most managers trade globally with a goal of generating steady returns with low volatility. This category includes interest rate swap arbitrage, U.S. and non-U.S. government bond arbitrage, forward yield curve arbitrage, and mortgage-backed securities (MBS) arbitrage. The mortgage-backed market is primarily U.S.-based, over-the-counter (OTC), and particularly complex. Note: Fixed income arbitrage is a generic description of a variety of strategies involving investments in fixed income instruments, and weighted in an attempt to eliminate or reduce exposure to changes in the yield curve. Risk Arbitrage Sometimes called merger arbitrage, this involves investment in event-driven situations such as leveraged buyouts (LBOs), mergers, and hostile takeovers. Normally, the stock c01.indd 6 1/10/08 11:00:55 AM Getting Started 7 of an acquisition target appreciates while the acquiring company’s stock decreases in value.

Everyone else who works at this fund came out of a banking program, and I was told that the reason they don’t normally hire consultants is that consultants typically don’t have any idea about finance. I believe I was able to make a case for myself because I knew the theory behind finance and had also taught myself the technical aspects. A lot of bankers know the technical part of finance and are Excel experts, but they don’t have the business intuition that consultants do. In my view, and I’m biased, I’d say a consultant who can understand yield curves, do a DCF analysis, and build a cash flow statement is in great shape to be a hedge fund candidate. My primary advice to someone aspiring to work at a hedge fund is to work to be at the top of your consulting or invest“People have to understand ment banking analyst class. Taking the time to invest in pubwhat each fund does before just lic securities will be a major differentiating factor. After that, saying that they are interested in if you can discuss the rationales of two or three investments hedge funds.” you’ve made, are comfortable with finance, and understand the macro issues affecting the markets, you will be in good shape.


The Rise of Carry: The Dangerous Consequences of Volatility Suppression and the New Financial Order of Decaying Growth and Recurring Crisis by Tim Lee, Jamie Lee, Kevin Coldiron

active measures, Asian financial crisis, asset-backed security, backtesting, bank run, Bernie Madoff, Bretton Woods, business cycle, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, collapse of Lehman Brothers, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, debt deflation, distributed ledger, diversification, financial intermediation, Flash crash, global reserve currency, implied volatility, income inequality, inflation targeting, labor-force participation, Long Term Capital Management, Lyft, margin call, market bubble, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, negative equity, Network effects, Ponzi scheme, purchasing power parity, quantitative easing, random walk, rent-seeking, reserve currency, rising living standards, risk/return, sharing economy, short selling, sovereign wealth fund, Uber and Lyft, uber lyft, yield curve

Central banks, uniquely, have control over their own funding costs, and it might seem unlikely that they would allow their own carry—the spread between the yields of the assets they hold and their funding cost that they set—to go negative. If an inflationary spiral were to inflict capital losses on them, they could counteract those capital losses by doubling down on their carry trades—buying more, now higher-yielding, bonds—as long as they kept the yield curve positively sloped, that is, kept their policy rate below the rising yields on long bonds. But it seems likely that, in such a spiral, keeping the yield curve positively sloped would exacerbate instead of arrest the spiral. Ultimately, they would then have to choose between restraining the inflation and maintaining their own solvency. The collapse of the central bank carry trade, which could also include carry trades associated with central bank liquidity swaps, could then render central banks insolvent.

The central bank will be relatively restrictive in its provision of high-powered liquidity (reserves), and this restriction of supply, set against strong bank demand for liquidity deriving from strong demand for credit, will force short-term interest rates upward. If inflation is high, longer-term interest rates will naturally be high also. To be an effective constraint, shorter-term interest rates will need to be at least as high as long-term interest rates (that is, the yield curve will be flat or downward sloping). If short-term interest rates are lower than long-term The Monetary Ramifications of the Carry Regime 111 interest rates, then the demand for credit at the short-term interest rate will still tend to be firm; inflation and growth will keep the demand for credit strong. If short-term interest rates are much lower than long-term rates, then the central bank is not being restrictive; in this case it must be supplying the liquidity (reserves) that the banks are demanding at a relatively favorable rate.


pages: 402 words: 110,972

Nerds on Wall Street: Math, Machines and Wired Markets by David J. Leinweber

AI winter, algorithmic trading, asset allocation, banking crisis, barriers to entry, Big bang: deregulation of the City of London, business cycle, butter production in bangladesh, butterfly effect, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, citizen journalism, collateralized debt obligation, corporate governance, Craig Reynolds: boids flock, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Danny Hillis, demand response, disintermediation, distributed generation, diversification, diversified portfolio, Emanuel Derman, en.wikipedia.org, experimental economics, financial innovation, fixed income, Gordon Gekko, implied volatility, index arbitrage, index fund, information retrieval, intangible asset, Internet Archive, John Nash: game theory, Kenneth Arrow, load shedding, Long Term Capital Management, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, market fragmentation, market microstructure, Mars Rover, Metcalfe’s law, moral hazard, mutually assured destruction, Myron Scholes, natural language processing, negative equity, Network effects, optical character recognition, paper trading, passive investing, pez dispenser, phenotype, prediction markets, quantitative hedge fund, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, Robert Metcalfe, Ronald Reagan, Rubik’s Cube, semantic web, Sharpe ratio, short selling, Silicon Valley, Small Order Execution System, smart grid, smart meter, social web, South Sea Bubble, statistical arbitrage, statistical model, Steve Jobs, Steven Levy, Tacoma Narrows Bridge, the scientific method, The Wisdom of Crowds, time value of money, too big to fail, transaction costs, Turing machine, Upton Sinclair, value at risk, Vernor Vinge, yield curve, Yogi Berra, your tax dollars at work

—OGDEN NASH T his chapter is based on a number of ever-evolving dinner and lunch talks I have given over many years, all called “Nerds on Wall Street” irrespective of their actual subject. Many financial conference speakers, including those talking to mixed professional/spousal audiences after open-bar events, are deadly dull; hardly anyone really wants to see yield curves over dessert and that last glass of wine. I started collecting photographs about markets and technology in the early 1990s, and tried to mix in some actual informative content. That, along with the natural sensibilities of a borscht belt comic, made me a popular alternative to the yield curve guys. Given the 20-minute rule for these talks, none of them were as voluminous as this chapter. Still, this is not intended in any way to be a complete history of market technology, but rather an easily digestible introduction. I occasionally still do these talks on what remains of greater Wall Street.

Note that these percentages are not referring to total energy consumption, but to the level of total power (the rate of delivering energy) that has to be provided over the year. In the electric world, this is called lowering the peak of the load duration curve. Understanding load duration curves is the first lecture in Power 101 class. If you want to understand bonds, you need to know about the yield curve. The load duration curve is equally important if you want to understand electricity. Figure 14.1 shows a load duration curve and how it would shift with the use of the technologies discussed in this chapter. Lowering the peaks on these curves is important economically, environmentally, and geopolitically, because the plants needed to meet them are expensive, and often oil fueled. Software applied to the electric grid offers unprecedented flexibility in reshaping the load duration curve.

The state of both means that there are likely to be more than 330 Nerds on Wall Str eet 1 2 Hourly MW Load Reduce Customers’ Peak Loads ⭈ Utility-Controlled Circuit-level Management Discharge Stored Power During Peak ⭈ Clean, Reliable, Efficient ⭈ Targeted Deployments 3 4 Offer Value-Added Optimize Generation Services and T&D Assets ⭈ Online Energy Management ⭈ Charge Energy Storage ⭈ Renewables Integration and PHEVs Off-peak 2 1 3 4 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 Hours Per Year Load duration curve. Result of stored power deployed during peak and charging power during off-peak. Result of reducing customers’ peak loads and energy conservation. ENVIRONMENTAL LEADERSHIP AND GRID RELIABILITY Figure 14.1 Reshaping the load duration curve. Bonds have the yield curve. Power has this. Source: GridPoint. a few readers contemplating this transition. This last chapter is a gentle introduction to and a survey of more in-depth resources on this topic. Accelerating Innovation There are over a million hybrid Toyota Prius vehicles on the road, and in Berkeley, California, it often seems that they are all parked on the same street. With only one model and a handful of colors, you need a distinctive bumper sticker to find yours.


pages: 263 words: 80,594

Stolen: How to Save the World From Financialisation by Grace Blakeley

"Robert Solow", activist fund / activist shareholder / activist investor, asset-backed security, balance sheet recession, bank run, banking crisis, banks create money, Basel III, basic income, battle of ideas, Berlin Wall, Big bang: deregulation of the City of London, bitcoin, Bretton Woods, business cycle, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collective bargaining, corporate governance, corporate raider, credit crunch, Credit Default Swap, cryptocurrency, currency peg, David Graeber, debt deflation, decarbonisation, Donald Trump, eurozone crisis, Fall of the Berlin Wall, falling living standards, financial deregulation, financial innovation, Financial Instability Hypothesis, financial intermediation, fixed income, full employment, G4S, gender pay gap, gig economy, Gini coefficient, global reserve currency, global supply chain, housing crisis, Hyman Minsky, income inequality, inflation targeting, Intergovernmental Panel on Climate Change (IPCC), Kenneth Rogoff, Kickstarter, land value tax, light touch regulation, low skilled workers, market clearing, means of production, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, negative equity, neoliberal agenda, new economy, Northern Rock, offshore financial centre, paradox of thrift, payday loans, pensions crisis, Ponzi scheme, price mechanism, principal–agent problem, profit motive, quantitative easing, race to the bottom, regulatory arbitrage, reserve currency, Right to Buy, rising living standards, risk-adjusted returns, road to serfdom, savings glut, secular stagnation, shareholder value, Social Responsibility of Business Is to Increase Its Profits, sovereign wealth fund, the built environment, The Great Moderation, too big to fail, transfer pricing, universal basic income, Winter of Discontent, working-age population, yield curve, zero-sum game

This has increased equity prices and US equity markets recently experienced what has been called the “longest bull run” in history.58 For these rising asset prices to be sustainable, investors have to believe that the corporations they are investing in will be profitable over the long term — unlikely for the average company given the investment and productivity trends outlined above. The implication is that stock markets are overvalued, and three factors suggest that investors know it. Firstly, stock markets are highly volatile — a tell-tale sign of a bubble.59 The second warning sign is what is called the “yield curve” — which shows the interest rates investors will receive on the same government bonds with different maturity dates. Longer-term bonds are supposed to have higher interest rates, because lending for a long time is riskier than for a short time, so investors need to be compensated with higher returns. But the yield curve has now inverted in the US, meaning that short-term yields are higher than long-term yields, indicating that investors are nervous about the future.60 Finally, the Buffett indicator — the market capitalisation to GDP ratio, or how big the stock markets are relative to the “real” economy — suggests that stock markets are overvalued.

50 Williams, A. (2017) “London House Prices Fall for the First Time Since 2009”, Financial Times, 29 September. 51 Office for National Statistics (2019) “UK House Price Index Summary: November 2018” 52 Office for National Statistics (2018) “Business Investment in the UK: July to September 2018 Revised Results” 53 Office for National Statistics (2019) “Insolvency Statistics — October to December 2018 (Q4 2018)” 54 Blakeley (2019). 55 World Bank (2018) “Gross fixed capital formation (annual % growth)” 56 IMF (2018) “Fiscal Monitor 2018: Capitalising on Good Times” 57 Oguh, C. and Tanzi, A. (2019) “Global Debt of $244 Trillion Nears Record Despite Faster Growth”, Bloomberg, 15 January. 58 Partington, R. (2018) “Wall Street Sets Record for Longest Bull Run in History”, Guardian, 22 August 59 Seeking Alpha (2019) ‘Stocks In 2019: Volatility Is Back’ https://seekingalpha.com/article/4234365-stocks-2019-volatility-back 60 Barrett E and Greifeld K (2019) ‘Treasuries Buying Wave Triggers First Curve Inversion Since 2007’ https://www.bloomberg.com/news/articles/2019-03-22/u-s-treasury-yield-curve-inverts-for-first-time-since-2007 61 Federal Reserve Bank of St Louis (2018) ‘Stock Market Capitalization to GDP for United States’ https://fred.stlouisfed.org/series/DDDM01USA156NWDB 62 Curran, E. (2018) “China’s Debt Bomb”, Bloomberg, 17 September. 63 Moody’s (2018) “Moody’s: China Shadow Banking Activity Increasingly Reveals Challenging Trade-Off Between Growth and Deleveraging”, Moody’s Investors Service, 3 December. 64 BIS (2019) 65 Banerjee, R. and Hofmann, B. (2018) “The Rise of Zombie Firms: Causes and Consequences”, Bank for International Settlements Quarterly Review, September. 66 Colombo, J. (2018) “The US Is Experiencing A Dangerous Corporate Debt Bubble”, Forbes, 29 August. https://www.forbes.com/sites/jessecolombo/2018/08/29/the-u-s-is-experiencing-a-dangerous-corporate-debt-bubble/#547ffa2f600e 67 Heath, M. (2018) “These May Be the World’s 10 Riskiest Housing Markets”, Bloomberg, 13 September. 68 Byres, W. (2012) “Basel III: Necessary, but Not Sufficient”, speech to the Financial Stability Institute’s 6th Biennial Conference on Risk Management and Supervision, Basel, 6 November 69 This account draws on: Laybourn-Langton, L., Rankin, L. and Baxter, D. (2019) “This is a Crisis: Facing up to the Age of Environmental Breakdown”, IPPR. http://www.ippr.org/research/publications/age-of-environmental-breakdown; Intergovernmental Panel on Climate Change (2018) “Global warming of 1.5°C.


The Trade Lifecycle: Behind the Scenes of the Trading Process (The Wiley Finance Series) by Robert P. Baker

asset-backed security, bank run, banking crisis, Basel III, Black-Scholes formula, Brownian motion, business continuity plan, business process, collapse of Lehman Brothers, corporate governance, credit crunch, Credit Default Swap, diversification, fixed income, hiring and firing, implied volatility, interest rate derivative, interest rate swap, locking in a profit, London Interbank Offered Rate, margin call, market clearing, millennium bug, place-making, prediction markets, short selling, statistical model, stochastic process, the market place, the payments system, time value of money, too big to fail, transaction costs, value at risk, Wiener process, yield curve, zero-coupon bond

These are short-term loans from overnight to nine or 12 months in Futures These are standard contracts for forward exchanges of money, where the rates are fixed today. They range from about three months to two years. Swaps These are quoted for longer periods extending up to 30 years or beyond. By combining prices for these three sets of instruments, we can derive a yield curve which indicates the yield (or interest rate) against time (see Figure 24.2). 313 Data 7 6 Yield (%) 5 4 3 2 1 0 0 5 10 15 20 Time to maturity (years) 25 30 35 FIGURE 24.2 Yield curve Surfaces Some instruments have three dimensions – two dimensions of data for any time period. In order to look at the shape of market data over time, we need to use a threedimensional object, usually referred to as a surface. A typical example is implied volatility. Although volatility is generally regarded as a reflection of the way prices move, it can also be a source of market data.

Bob Steiner (2012) Mastering Financial Calculations: A Step-by-step Guide to the Mathematics of Financial Market Instruments, Financial Times/Prentice Hall. 377 Index 30/360 date calculation 350–1 ABSs see asset backed securities abusive behaviour, traders 223 acceptance testing see user acceptance testing accounting 161–9 balance sheet 161–4 financial reports 168–9 profit and loss account 164–8 accrual accrual convention 349–50 accrued profit and loss 165 actual/actual date calculation 350 advisory services 269, 370 aggregation of calculations 342 trades 101–2 agricultural commodities 56 algorithms 184 amendment to a trade 108 American options 29, 66 amortising bonds 47, 48 analytics 271–2 see also quantitative analysts animal products 56 application programming interface (API) 270 architects, IT 187 asset backed securities (ABSs) 47 asset classes 33–59 bonds and credit 46–53 commodities 29, 53–8 equities 44–5 foreign exchange 40–4 interest rates 33–40 and products 17 trade matrix 71–2 trading across 58–9 asset holdings see holdings asset managers 10, 168–9 at-the-money options 66 audit 191–2 average trades, exotic options 68 back book trading 132 back office (operations) 183, 227, 316 back testing 317 back-to-back trades 152 bad data 105, 317–20 balance sheet 161–4 banks culture and conduct 203 interbank systems 158 reasons for trading 9–10 retail banks 222 traders’ internal accounts 123 Barclays Capital 219 barrier options 68 base rate, interest rates 35 Basel II 144 Basel III 205 baskets exotic options 68 FX trades 41–2 BCP see business continuity planning bearer securities 124 Bermudan options 66 bespoke trades 69–70 bid/offer spread 310 binary options 68–9 black box (mathematical library) 238, 241, 270 black box testing 301 Black Scholes formula 346 board of directors 193–4 bond basis deltas 175 379 380 bonds 27, 28, 29, 46–53 coupon payments 47, 48, 106–7 RABOND project case study 225–35 sovereign debt 46 tradeflow issues 49 types 46–7 bonuses 220–1 booking of a trade 85, 93–4 bootstrapping 348–9 boundary testing 351 breaches, dealing with 155–6 breaks, settlement 356–7 brokers 5–6, 10, 75 buckets (time intervals) 148–9 bullying behaviour 223 business continuity planning (BCP) 373 calculation process 337–52 see also valuation process bootstrapping 348–9 calibration to market 351 dates 349–51 example 337–8 mark-to-market value 339–40 model integration 352 net present value 338–9, 343–8 risks 352 sensitivity analysis 347–8 calibration process, valuation 351 call options 62, 63 cancellation of a trade 109 capital adequacy ratio (CAR) 144 case studies 225–52 EcoRisk project 235–47 OTTC equity confirmation project 247–52 RABOND project 225–35 cash balance sheet item 162 exchange dates 86 exercise 111 settlement 98, 99 cashflows American options 30 asset holdings 117–24 bank within a bank 123 consolidated reporting 122 custody of securities 123–4 diversification 122–3 realised and unrealised P&L 122 INDEX reconciliation 121 risks 124 treatment of 119–20 value of 120–1 credit default swaps 31 deposits 23 discount curve 38–9 equity spot trades 26 fixed bonds 27, 28 floating bonds 27, 28 foreign exchange swaps 25 future trades 20–1 loans 22 options 27–30, 345–6 post booking 96–7 risks 367 spot trades 19 swap trades 24 unknown, options valuation 345–6 zero bonds 27, 29 CDSs see credit default swaps Central Counterparty Clearing (CCP) 210–12 change coping with 260–1, 284 to a trade 105–10 clearing 210–12 Cliquet (ratchet options) 68 collateral 108, 153–4, 156, 212–13 COM (common object model) 246 commodities 29, 53–8 cash settlement 99 characteristics 55–6 currency 57 definition 55 example 53–5 localised production 57 OTC commodities 56 physical settlement 57–8 profit curve 54–5 time lag 57 tradeflow issues 58 types 56 utility of 57 common object model (COM) 246 communication 188, 197–8, 254–5, 259–60, 305, 371 competition analysis 269 compliance officers 192–3 confirmation of a trade 94–6, 247–52, 355 conflicts and tensions 196–7, 198–9, 360–1 381 Index consolidated reporting 122 consolidation of processes 283–4 control see also counterparty risk control; market risk control people involved in 189–99, 224 of report generation 335 conversion, currency 344 correlation risk 131, 363 counterparties changes to a trade 108 correlation between 364 identification of 85 Counterparty Clearing, Central 210–12 counterparty risk control 147–60, 364–5 activities of department 154–7, 190–1 collateral 153–4, 156 counterparty identification 153 default consequences 148 limit imposition 152–3 management interface 157 measurement of risk 149–52, 155, 156 non-fulfilment of obligations 147–8 payment systems 158–60 quantitative analyst role 268 risks in analysing credit risk 157–8 settlement 356 time intervals 148–9 coupon payments, bonds 47, 48, 106–7 credit default swaps (CDSs) 30–1, 51–2, 65–6, 175, 209 credit exposure 150–1 credit quantitative analysts 274 credit rating companies 231–2 credit risk see also counterparty risk control; credit default swaps; credit valuation adjustment bonds 46–53 default 51 definition 50 documentation 50–1 market data 316 measurement of 209 recovery rate 52–3 risks in analysing 157–8 types of risk 131 credit valuation adjustment (CVA) 207–13 debt valuation adjustment 209 definition 208 funding valuation adjustment 209 measurement of 208 mitigation 210 netting 211–12 portfolio-based 213 rehypothication 212–13 credit worthiness 51–2, 155 creditors, balance sheet item 163 CreditWatch 232 culture of banks 203 currency conversion 344 exposure to 4 precious metals as 57 reporting currency 42 value of holdings 120–1 currency swaps, foreign exchange 41 current (live) market data 79, 314 curves, market data 310–13 custodians 98, 124 customer loyalty 199 CVA see credit valuation adjustment data 307–25 absence of 368 authentic data 368 back testing 317 bad data 105, 317–20 bid/offer spread 310 corrections to 321–2 data feeds 226 expectations 309–10 extreme values 317 implied data 323 integrity of 322–4 internal data 321 interpolation 319 market data 107–8, 180, 292, 308–17 processes 286 risks 324–5, 367–8 sources of 320–1, 323 storage 309 testing 302 time series analysis 320 types of 308–10 validity of 307 vendors 321 data cleaning 320 data discovery 319–20 data engineering 319 databases 250–1, 308 382 dates calculation of 349–51 exercise of trades 111 final settlement 113 internal and external trades 102 relating to a trade 86–7 settlement 101, 113 on trade tickets 102 debt, exposure to 127 debt valuation adjustment (DVA) 209 debtors, balance sheet item 162 default 51, 131, 148 see also credit default swaps delivery versus payment (DvP) 98 delta hedging 133 delta risk 130 deltas 175 deposits 23, 35 derivatives 61–72 see also futures and forwards; options; swaps digital options 68–9 directors, role of 193–4 discount curve, interest rates 38–9 discounting, NPV calculation 343–4, 345 diversification 122–3 dividends 105–6 documentation credit risk 50–1 EcoRisk project case study 240–1 legal documents 84–5 processes 287 risks 356, 374 settlement 98 Dodd–Frank Act 206–7 dreaming ahead 131–2 due diligence 192, 292 duties (fees) 97 DVA (debt valuation adjustment) 209 DVO1, risk measure 138 DvP (delivery versus payment) 98 economic data 84 EcoRisk project, case study 235–47 documentation 240–1 functionality 243–4 Graphical User Interface 237–8 mathematical library 238, 241 solution 238–40 testing 239–40 valuation problem debugging 242–3 INDEX electronic exchanges 6 electronic systems 92 email 92 EMIR (European Markets Infrastructure Regulation) 202–3 employees see people involved in trade lifecycle end of day roll 103, 181–2 end of month reports 182 energy products 56 equal opportunities 219–20 equities 26, 44–5, 247–52 errors confirmation process 95 in data 322 P&L corrections 171 post booking 97 in reports 333–4 European Markets Infrastructure Regulation (EMIR) 202–3 European options 29, 66 exceptions, processes 322 exchange price 75 exchanges 6, 86, 320 execution of a trade 89–93 exercise, option trades 64, 110–12, 357–8 exotic options 67–9, 109, 235–47, 346 expected loss 150 exposure 4, 125–8, 130–2, 150–1, 155, 156 fault logging 302–4 fees 97, 169 finance department 191, 316 financial products 17–31 bonds 27, 28, 29, 46–53, 106–7, 225–35 credit default swaps 30–1, 51–2, 65–6, 175, 209 deposits 23, 35 equities 26, 44–5, 247–52 futures 20–1, 35–6, 40–1, 61, 62, 77, 127, 311, 312 FX swaps 25, 41 loans 21–3 options 27–30, 61–9, 77, 109–12, 127, 235–47, 345–6, 357–8 spot trades 18–19, 40, 127 swaps 23–5, 30–1, 36–7, 41, 107, 312–13 financial reports 168–9 financial services industry 8–10 fixed assets, balance sheet item 161 fixed bonds 27, 28, 47, 48 fixed and floating coupons 127 383 Index fixed for floating swaps 23–4 fixed loans 22 fixing date 86 fixings 107–8 float for fixed/float for float 36 floating bonds 27, 28 floating loans 22 floating rate notes (FRNs) 47 flow diagrams 287 FoP (free of payment) 98 foreign exchange (FX) 40–4 baskets 41–2 FX drift 42–3 reporting currency 42 swaps 25, 41 tradeflow issues 43–4 forward rate agreement (FRA) 37–8 see also futures and forwards free of payment (FoP) 98 FRNs (floating rate notes) 47 front book trading 132 front line support staff 186 front office EcoRisk project, case 235–47 market risk control 142 risks 375–6 fugit 112 fund managers 10 funding valuation adjustment (FVA) 209 futures and forwards 20–1, 35–6, 40–1, 61, 62 gold futures 311, 312 leverage 77 risks 127 FVA (funding valuation adjustment) 209 FX see foreign exchange gamma risk 130 gearing 77–8 gold futures 311, 312 governance 204 Graphical User Interface (GUI) 237–8 hedge funds 10, 168–9, 212–13 hedging strategies 133–4 hedging trades 128 help desks 247 historical market data 314 holdings 117–24 asset types 118 bank within a bank 123 consolidated reporting 122 custody of securities 123–4 diversification 122–3 realised and unrealised P&L 122 reconciliation 121 risks 124 value 120–1 human resources see people involved in trade lifecycle human risks 194–9, 359–61 hybrid trades 69–70 identification details, trades 83–4 illiquid products 140 illiquid trades 339 in person trades 92 in-the-money options 66 incentives 195 industrial metals 56 information technology (IT) architects 187 case studies 225–52 communication 259–60 dependency on 284 EcoRisk project 235–47 equity confirmation project 247–52 infrastructure 186 IT divide 253–66 business functions 255–6 business requirements 261–3 coping with change 260–1 do’s and don’ts 263 IT blockers 258 IT requirements 263–4 misuse of IT 256–7 organisational blockers 257–8 problems caused by 255 project examples 265–6 solution 259–60 language of 254 legacy systems 282 operators 188 project managers 187–8 quality control 260 and quantitative analysts 271–4 RABOND project 225–35 risks 375–6 staff 185–9, 197, 217–18, 253–66 testers 188–9 and traders 258 384 infrastructure, IT 186 instantaneous risk measures 138 insurance 30–1, 50 integration testing 300 interbank systems 158 interbank trading (LIBOR) 39 interest rates 21–3, 33–40 base rate 35 credit effects 39 deltas 175 deposits 35 discount curve 38–9 forward rate agreement 37–8 futures 35–6, 311–12 market participants 34–5 option valuation 67 products 35–8 quantitative analysts 274 swaps 23–5, 36–7 time value of money 33–4 tradeflow issues 39–40 vegas 175 interim delivery of projects 259 internal audit 191–2 International Swaps and Derivatives Association (ISDA) 50 investment banks 9–10 investments, balance sheet item 161 ISDA (International Swaps and Derivatives Association) 50 IT see information technology kappa risk 130 knock in/knock out, barrier options 68 knowledge, risks 359–60 legacy IT systems 282 legal department 189, 293, 316 legal documents 84–5 legal risks 50, 369 leverage 64–6, 76–9 LIBOR (interbank trading) 39 libraries 184–5 lifecycle of a trade see trade lifecycle limit orders 129 limits and credit worthiness 155 imposing 152–3 market risk control 141 line managers 222 INDEX linear derivatives 61, 62 liquidity 73–5, 202, 375 litigation 370 live trading 7 loans 21–3 management see also project management; risk management of changes 109–10 counterparty risk control 157 fees 169 market data usage 317 new products 292 responsibilities of 193–4 risks 374 of teams 229–31 margin payments 156 mark-to-market value 339–40 market data 180, 292, 308–17 business usage 315–17 changes as result of 107–8 curves and surfaces 310–13 sets of 314 market participants 4–5 market risk control 135–45, 190, 363–4 allocation of risk 139 balanced approach 143 controlling the risk 140–1 human factor 143 limitations 142–3 market data usage 316 methodologies 135–9 monitoring of market risk 140 need for risk 139 quantitative analyst role 268 regulatory requirements 143–4 responsibilities 141–2 market sentiment 340 matching of records 94–5 mathematical libraries 238, 241, 270 mathematical models evolution of 343 new products 293 parameters 341 prototypes 238–9 quantitative analyst role 183–5 risks 373 validation team 189–90 maturity of a trade 8, 67, 86, 112–13 MBS see mortgage backed securities 385 Index metal commodities 56, 57 middle office (product control) market data usage 316 new products 293 RABOND project, case study 225–35 role of 180–2 missing data 317 mobile phones 92 models see mathematical models Monte Carlo technique 346–7 mortgage backed securities (MBS) 47 multilateral netting 211–12 NatWest Markets, EcoRisk project 235–47 net present value (NPV) 338–9, 343–8 netting 152, 211–12 new products 289–95 checklist 292–3 evolution of 294 market data 292 market risk control 140 process development/improvement 279–88 risks 194, 294–5, 369 testing 291–2 trial basis for 290–2 new trade types 156 nonlinear derivatives 62–9 nostro accounts 99 NPV see net present value official market data 314 offsetting of risks 128 OIS (overnight indexed swap) 39 operational risks 355–8 operations department 183, 227, 316 operators, IT 188 options 27–30, 61–9 credit default swaps 65–6 exercise 110–12 exotic options 67–9, 109, 235–47, 346 leverage 64–6, 77 risks 127, 357–8 terminology 66 trade process 64–6 valuation 67, 345–6 orders 90–1, 357 OTC see over-the-counter trading OTTC equity confirmation project, case study 247–52 out-of-the-money options 66 over-the-counter (OTC) trading 6–7 clearing 210 commodities 56 price 75 overnight indexed swap (OIS) 39 overnight processes 101–5 P&L see profit and loss parallel testing 301 pay 203, 220–1 payment systems 106–7, 158–60, 357 pension funds 10 people involved in trade lifecycle 177–200 see also working in capital markets back office 183, 227, 316 compliance officers 192–3 conflicts and tensions 196–9, 360–1 control functions 189–99 counterparty risk control department 190–1 finance department 191, 316 human risks 194–9 information technology 185–9, 197, 217–18, 253–66 internal audit 191–2 legal department 189, 293, 316 line managers 222 management 193–4 market risk control department 190 middle office 180–2, 225–35, 293, 316 model validation team 189–90 personality and outlook 194–5, 244–5, 273 programmers 187, 244–5 quantitative analysts 183–5, 267–75 researchers 179–80 revenue generation 177–89 sales department 179, 227, 315, 375 senior managers 126 staffing levels 195 structurers 179 supervisors 204 testers 298–9 traders 125–6, 177–8, 218–23, 226–7, 258, 268, 315, 361 trading assistants 178 trading managers 126, 193 training of staff 193 performance reports 169 personality and outlook 194–5, 244–5, 273 PFE (potential future exposure) 151 physical assets, exercise 111 386 physical commodities, settlement 57–8, 99 planning of processes 282–3 recovery plans 203–4 risks 360 post booking processes 96–7 postal trades 92–3 potential future exposure (PFE) 151 power, abuses of 220, 221–2 pre-execution of a trade 89–91 precious metals 56, 311, 312 premiums 31 price 75–6, 138–9 pricing methods EcoRisk project, case study 235–47 short-term pricing 183 process development/improvement 279–88 coping with change 284 current processes 285–7 evolution of processes 280–1 improving the situation 284–7 inertia 287–8 inventory of current systems 282–4 planning 282–3 timing 288 producers 5 product appetite 4 product control see middle office product development see new products profit curve, commodity trading 54–5 profit and loss (P&L) accounts 164–8 accrued and incidental 165 example 165–6 individual trades 166–7 realised and unrealised 165 responsibility for producing 167 risks associated with reporting 167–8 rogue trading 168 attribution reports 171–6 benefits of 171–2 example 173–6 market movements 173, 175 process 172–3 unexplained differences 173 balance sheet item 163 end of day 182 realised and unrealised 122, 165 programmers 187, 244–5 see also quantitative analysts INDEX project management 225–47, 259, 262 project managers 187–8 proprietary (‘prop’) trading 203 prototypes, IT projects 238–9 provisional trades 89–90, 357–8 put options 62, 63 PVO1, risk measure 138 quality control, IT 260 see also testing quantitative analysts (quants) 183–5, 267–75 and IT professionals 271–4 role of 267–9, 270 seating arrangements 270–1 working methods 269–70 RABOND project, case study 225–35 management 229–31 outcome 233–5 reports 227–9 team management 229–31 traders 226–7 random market data 314 rapid application development (RAD) 260, 281 ratchet options (Cliquet) 68 ratings companies 231–2 raw data 323 raw reporting 331 real world of capital markets see working in capital markets realised P&L 122 receipts 156 reconciliation 121 recovery plans 203–4 recovery rates 52–3, 176 redundancy, processes 282 reform of banks 203 registered securities 123–4 regression testing 302 regulation 201–13, 223–4 authorities 202 Basel II and III 144, 205 credit valuation adjustment 207–13 external 192 internal 224 market risk control 143–4 new products 293 problems 204–5 requirements 202–4 387 Index risk-weighted assets 205–7 risks 369 rehypothication 212–13 remuneration 203, 220–1 reporting currency 42 reports 327–36 accuracy 330–1, 368 calculation process 342 configuration 331–2 consolidated reporting 122 content 328–9 control issues 335 dimensions 333 distribution 329–30, 335, 369 dynamic reports 332–3 end of month reports 182 enhancements 335 errors in 333–4 false reporting 375 financial reports 168–9 frame of reference 333 middle office role 180–1 OTTC equity confirmation project 250, 251 performance reports 169 presentation 329 problems 333–4 profit and loss 167, 171–6 RABOND project 227–9 raw reporting 331 readership 328, 329, 368–9 redundancy of 334–5 requirements 328–33 risks 335–6, 368–70 security issues 335, 368 timing 330 types of 330 reputation, risk to 356, 370 research 268, 375 researchers 179–80 reserve accounts 141 reset date 86 resettable strike, exotic options 68 retail banks 222 revenue generation, people involved in 177–89 rho risk 130 risk 13–16 see also counterparty risk control; market risk control advisory services 370 appetite for 4 business continuity planning 373 calculation process 352 cashflows 124, 367 changes to a trade 110 communication 371 confirmation 95–6, 355 control departments 224 correlation 363 data 324–5, 367–8 definition 13 documentation 356, 374 exercise 112 front office 375–6 human risks 194–9, 359–61 information technology 375–6 instantaneous measures 138 legal risks 369 liquidity 74–5, 375 litigation 370 management risks 374 measures 130, 138, 149–52, 155, 156 model approval 373 new products 140, 194, 294–5, 369 operational risks 355–8 orders 91 origin of risks 126–8 payment systems 357 provisional trades 90, 357–8 quantifying 14 regulation 369 reports 335–6, 368–70 reputation 356, 370 risk-weighted assets 205–7 sales 375 settlement 100, 355–7 short-term thinking 360 straight through processing 357 support activities 376 systematic 202–3, 375–6 testing 304–5, 370–1 types 130–2 unexpected charges 356 unforeseen 16, 353 valuation process 352, 373 risk management 13, 15, 125–34 in absence of trader 128–9 dreaming ahead 131–2 EcoRisk project case study 235–47 hedging strategies 133–4 hedging trades 128 388 risk management (continued) offsetting of risks 128 senior managers 126 traders 125–6, 361 trading managers 126 trading strategies 132 rogue trading 168 sales data 84 sales department 179, 227, 315, 375 SBC Warburg, equity confirmation project, case study 247–52 scenario analysis 136, 198–9, 341 scope creep 187, 264 scrutiny of trades 96 securities, custody of 123–4 security issues 181, 335, 368 semi-static data 309 senior managers 126 sensitivity analysis 138, 347–8 settlement 97–101, 147–8 breaks 101, 356–7 commodities 99 dates 101, 113 nostro accounts 99 quick settlement 101 risks 100, 355–7 shares 44–5 see also equities short selling 65 short-term pricing 183 short-term thinking 195–6, 360 silo approach 257 simple products 70–1 smoke testing 301 sovereign debt 46 speculators 5 spot prices 61, 62, 63, 67, 76–7 spot testing 301 spot trades 18–19, 40, 127 spread of bid/offer 310 spreadsheets 184, 238 staff see people involved in trade lifecycle stale data 105, 318 Standard & Poor’s (S&P) ratings 231–2 static data 309 stop-loss hedging 133–4 stop orders 129 storage of data 309 straight through processing (STP) 93–4, 357 INDEX stress, staff 222, 244–5 stress testing 302 strike price, options 67 structured trades 69–70 structurers 179 supervisors 204 support activities, risks 376 surfaces, market data 310–13 swaps credit default 30–1, 51–2, 65–6, 175, 209 fixings 107 foreign exchange 25, 41 interest rate 23–5, 36–7 yield curves 312–13 swaptions 66 synthetic equities (index) 45 systems see also information technology amalgamation 104–5 analytics 271–2 electronic systems 92 integrated 261 legacy IT systems 282 risks 375–6 testing 251–2, 300 tail behaviour, predicting 143, 364 team management 229–31 telephone transactions 91–2 tensions and conflicts 196–9, 360–1 testing 297–305 back testing 317 boundary testing 351 extreme values 352 fault logging 302–4 importance of 298 mathematical models 239 new products 291–2 risks 304–5, 370–1 stages 300–1 testers 188–9, 298–9 types of 301–2 unit testing 300 user acceptance testing 237, 239–40, 252, 264, 301 when to perform 299–300 theft 355 theta risk 130 time intervals (buckets) 148–9 time lag, commodities 57 389 Index time series analysis 320 timeline of a trade 79, 86–7 trade blotters 93 trade lifecycle 89–115 booking 93–4 business functions 11 changes during lifetime 105–10 confirmation 94–6 equity trades 45 example trade 113–15 execution 91–3 exercise 110–12 maturity 112–13 new products 293 overnight processes 101–5 post booking 96–7 pre execution 89–91 settlement 97–101 trade tickets 102 trade/trading 3–12 see also trade lifecycle anatomy 83–7 business functions 11 complicated trades 340 consequences of 7–8 definition 10–12 financial products 17–31 live trading 7 matching of records 94–5 policies 8 reasons for 3, 9–10 timeline 79, 86–7 transactions 5–7 types 132 tradeflow issues bonds 49 commodities 58 foreign exchange 43–4 interest rates 39–40 traders 177–8, 218–22, 223, 226–7, 258, 268 bonuses 220–1 market data usage 315 risk management 125–6, 361 trading assistants 178 trading desks 70–1, 256–7 trading floor 217–18, 235–6 trading managers 126, 193 training of staff 193 tranche correlation 131 treasury desk 71 trials for new products 290–2 trust 197, 222 UAT see user acceptance testing underlying 83 unexplained differences, P&L reports 173 unforeseen risk 16, 353 unit testing 300 unknown cashflows 345–6 unrealised P&L 122 unwinding a trade, cost of 76 user acceptance testing (UAT) 237, 239–40, 252, 264, 301 validation of models 189–90 valuation process see also calculation process calibration to market 351 mark-to-market value calculation 339–40 middle office role 181 NPV calculation 338–9, 343–8 options 67 problem debugging 242–3 risks 352, 364, 373 valuation systems 269 value at risk (VaR) 136–8, 341 vega (kappa) risk 130 vegas 175 vendors, data services 321 volatility 67, 130 volume of a trade, price effect 76 white box testing 301 workarounds 303 working in capital markets 217–24 see also case studies; people involved in trade lifecycle in 1990s 217–19 culture clashes 219 equal opportunities 219–20 office politics 220–2, 246 positive/negative aspects 222–3 yield curves 312–13 zero bonds 27, 29, 47 Index compiled by Indexing Specialists (UK) Ltd WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley’s ebook EULA.

Bob Steiner (2012) Mastering Financial Calculations: A Step-by-step Guide to the Mathematics of Financial Market Instruments, Financial Times/Prentice Hall. 377 Index 30/360 date calculation 350–1 ABSs see asset backed securities abusive behaviour, traders 223 acceptance testing see user acceptance testing accounting 161–9 balance sheet 161–4 financial reports 168–9 profit and loss account 164–8 accrual accrual convention 349–50 accrued profit and loss 165 actual/actual date calculation 350 advisory services 269, 370 aggregation of calculations 342 trades 101–2 agricultural commodities 56 algorithms 184 amendment to a trade 108 American options 29, 66 amortising bonds 47, 48 analytics 271–2 see also quantitative analysts animal products 56 application programming interface (API) 270 architects, IT 187 asset backed securities (ABSs) 47 asset classes 33–59 bonds and credit 46–53 commodities 29, 53–8 equities 44–5 foreign exchange 40–4 interest rates 33–40 and products 17 trade matrix 71–2 trading across 58–9 asset holdings see holdings asset managers 10, 168–9 at-the-money options 66 audit 191–2 average trades, exotic options 68 back book trading 132 back office (operations) 183, 227, 316 back testing 317 back-to-back trades 152 bad data 105, 317–20 balance sheet 161–4 banks culture and conduct 203 interbank systems 158 reasons for trading 9–10 retail banks 222 traders’ internal accounts 123 Barclays Capital 219 barrier options 68 base rate, interest rates 35 Basel II 144 Basel III 205 baskets exotic options 68 FX trades 41–2 BCP see business continuity planning bearer securities 124 Bermudan options 66 bespoke trades 69–70 bid/offer spread 310 binary options 68–9 black box (mathematical library) 238, 241, 270 black box testing 301 Black Scholes formula 346 board of directors 193–4 bond basis deltas 175 379 380 bonds 27, 28, 29, 46–53 coupon payments 47, 48, 106–7 RABOND project case study 225–35 sovereign debt 46 tradeflow issues 49 types 46–7 bonuses 220–1 booking of a trade 85, 93–4 bootstrapping 348–9 boundary testing 351 breaches, dealing with 155–6 breaks, settlement 356–7 brokers 5–6, 10, 75 buckets (time intervals) 148–9 bullying behaviour 223 business continuity planning (BCP) 373 calculation process 337–52 see also valuation process bootstrapping 348–9 calibration to market 351 dates 349–51 example 337–8 mark-to-market value 339–40 model integration 352 net present value 338–9, 343–8 risks 352 sensitivity analysis 347–8 calibration process, valuation 351 call options 62, 63 cancellation of a trade 109 capital adequacy ratio (CAR) 144 case studies 225–52 EcoRisk project 235–47 OTTC equity confirmation project 247–52 RABOND project 225–35 cash balance sheet item 162 exchange dates 86 exercise 111 settlement 98, 99 cashflows American options 30 asset holdings 117–24 bank within a bank 123 consolidated reporting 122 custody of securities 123–4 diversification 122–3 realised and unrealised P&L 122 INDEX reconciliation 121 risks 124 treatment of 119–20 value of 120–1 credit default swaps 31 deposits 23 discount curve 38–9 equity spot trades 26 fixed bonds 27, 28 floating bonds 27, 28 foreign exchange swaps 25 future trades 20–1 loans 22 options 27–30, 345–6 post booking 96–7 risks 367 spot trades 19 swap trades 24 unknown, options valuation 345–6 zero bonds 27, 29 CDSs see credit default swaps Central Counterparty Clearing (CCP) 210–12 change coping with 260–1, 284 to a trade 105–10 clearing 210–12 Cliquet (ratchet options) 68 collateral 108, 153–4, 156, 212–13 COM (common object model) 246 commodities 29, 53–8 cash settlement 99 characteristics 55–6 currency 57 definition 55 example 53–5 localised production 57 OTC commodities 56 physical settlement 57–8 profit curve 54–5 time lag 57 tradeflow issues 58 types 56 utility of 57 common object model (COM) 246 communication 188, 197–8, 254–5, 259–60, 305, 371 competition analysis 269 compliance officers 192–3 confirmation of a trade 94–6, 247–52, 355 conflicts and tensions 196–7, 198–9, 360–1 381 Index consolidated reporting 122 consolidation of processes 283–4 control see also counterparty risk control; market risk control people involved in 189–99, 224 of report generation 335 conversion, currency 344 correlation risk 131, 363 counterparties changes to a trade 108 correlation between 364 identification of 85 Counterparty Clearing, Central 210–12 counterparty risk control 147–60, 364–5 activities of department 154–7, 190–1 collateral 153–4, 156 counterparty identification 153 default consequences 148 limit imposition 152–3 management interface 157 measurement of risk 149–52, 155, 156 non-fulfilment of obligations 147–8 payment systems 158–60 quantitative analyst role 268 risks in analysing credit risk 157–8 settlement 356 time intervals 148–9 coupon payments, bonds 47, 48, 106–7 credit default swaps (CDSs) 30–1, 51–2, 65–6, 175, 209 credit exposure 150–1 credit quantitative analysts 274 credit rating companies 231–2 credit risk see also counterparty risk control; credit default swaps; credit valuation adjustment bonds 46–53 default 51 definition 50 documentation 50–1 market data 316 measurement of 209 recovery rate 52–3 risks in analysing 157–8 types of risk 131 credit valuation adjustment (CVA) 207–13 debt valuation adjustment 209 definition 208 funding valuation adjustment 209 measurement of 208 mitigation 210 netting 211–12 portfolio-based 213 rehypothication 212–13 credit worthiness 51–2, 155 creditors, balance sheet item 163 CreditWatch 232 culture of banks 203 currency conversion 344 exposure to 4 precious metals as 57 reporting currency 42 value of holdings 120–1 currency swaps, foreign exchange 41 current (live) market data 79, 314 curves, market data 310–13 custodians 98, 124 customer loyalty 199 CVA see credit valuation adjustment data 307–25 absence of 368 authentic data 368 back testing 317 bad data 105, 317–20 bid/offer spread 310 corrections to 321–2 data feeds 226 expectations 309–10 extreme values 317 implied data 323 integrity of 322–4 internal data 321 interpolation 319 market data 107–8, 180, 292, 308–17 processes 286 risks 324–5, 367–8 sources of 320–1, 323 storage 309 testing 302 time series analysis 320 types of 308–10 validity of 307 vendors 321 data cleaning 320 data discovery 319–20 data engineering 319 databases 250–1, 308 382 dates calculation of 349–51 exercise of trades 111 final settlement 113 internal and external trades 102 relating to a trade 86–7 settlement 101, 113 on trade tickets 102 debt, exposure to 127 debt valuation adjustment (DVA) 209 debtors, balance sheet item 162 default 51, 131, 148 see also credit default swaps delivery versus payment (DvP) 98 delta hedging 133 delta risk 130 deltas 175 deposits 23, 35 derivatives 61–72 see also futures and forwards; options; swaps digital options 68–9 directors, role of 193–4 discount curve, interest rates 38–9 discounting, NPV calculation 343–4, 345 diversification 122–3 dividends 105–6 documentation credit risk 50–1 EcoRisk project case study 240–1 legal documents 84–5 processes 287 risks 356, 374 settlement 98 Dodd–Frank Act 206–7 dreaming ahead 131–2 due diligence 192, 292 duties (fees) 97 DVA (debt valuation adjustment) 209 DVO1, risk measure 138 DvP (delivery versus payment) 98 economic data 84 EcoRisk project, case study 235–47 documentation 240–1 functionality 243–4 Graphical User Interface 237–8 mathematical library 238, 241 solution 238–40 testing 239–40 valuation problem debugging 242–3 INDEX electronic exchanges 6 electronic systems 92 email 92 EMIR (European Markets Infrastructure Regulation) 202–3 employees see people involved in trade lifecycle end of day roll 103, 181–2 end of month reports 182 energy products 56 equal opportunities 219–20 equities 26, 44–5, 247–52 errors confirmation process 95 in data 322 P&L corrections 171 post booking 97 in reports 333–4 European Markets Infrastructure Regulation (EMIR) 202–3 European options 29, 66 exceptions, processes 322 exchange price 75 exchanges 6, 86, 320 execution of a trade 89–93 exercise, option trades 64, 110–12, 357–8 exotic options 67–9, 109, 235–47, 346 expected loss 150 exposure 4, 125–8, 130–2, 150–1, 155, 156 fault logging 302–4 fees 97, 169 finance department 191, 316 financial products 17–31 bonds 27, 28, 29, 46–53, 106–7, 225–35 credit default swaps 30–1, 51–2, 65–6, 175, 209 deposits 23, 35 equities 26, 44–5, 247–52 futures 20–1, 35–6, 40–1, 61, 62, 77, 127, 311, 312 FX swaps 25, 41 loans 21–3 options 27–30, 61–9, 77, 109–12, 127, 235–47, 345–6, 357–8 spot trades 18–19, 40, 127 swaps 23–5, 30–1, 36–7, 41, 107, 312–13 financial reports 168–9 financial services industry 8–10 fixed assets, balance sheet item 161 fixed bonds 27, 28, 47, 48 fixed and floating coupons 127 383 Index fixed for floating swaps 23–4 fixed loans 22 fixing date 86 fixings 107–8 float for fixed/float for float 36 floating bonds 27, 28 floating loans 22 floating rate notes (FRNs) 47 flow diagrams 287 FoP (free of payment) 98 foreign exchange (FX) 40–4 baskets 41–2 FX drift 42–3 reporting currency 42 swaps 25, 41 tradeflow issues 43–4 forward rate agreement (FRA) 37–8 see also futures and forwards free of payment (FoP) 98 FRNs (floating rate notes) 47 front book trading 132 front line support staff 186 front office EcoRisk project, case 235–47 market risk control 142 risks 375–6 fugit 112 fund managers 10 funding valuation adjustment (FVA) 209 futures and forwards 20–1, 35–6, 40–1, 61, 62 gold futures 311, 312 leverage 77 risks 127 FVA (funding valuation adjustment) 209 FX see foreign exchange gamma risk 130 gearing 77–8 gold futures 311, 312 governance 204 Graphical User Interface (GUI) 237–8 hedge funds 10, 168–9, 212–13 hedging strategies 133–4 hedging trades 128 help desks 247 historical market data 314 holdings 117–24 asset types 118 bank within a bank 123 consolidated reporting 122 custody of securities 123–4 diversification 122–3 realised and unrealised P&L 122 reconciliation 121 risks 124 value 120–1 human resources see people involved in trade lifecycle human risks 194–9, 359–61 hybrid trades 69–70 identification details, trades 83–4 illiquid products 140 illiquid trades 339 in person trades 92 in-the-money options 66 incentives 195 industrial metals 56 information technology (IT) architects 187 case studies 225–52 communication 259–60 dependency on 284 EcoRisk project 235–47 equity confirmation project 247–52 infrastructure 186 IT divide 253–66 business functions 255–6 business requirements 261–3 coping with change 260–1 do’s and don’ts 263 IT blockers 258 IT requirements 263–4 misuse of IT 256–7 organisational blockers 257–8 problems caused by 255 project examples 265–6 solution 259–60 language of 254 legacy systems 282 operators 188 project managers 187–8 quality control 260 and quantitative analysts 271–4 RABOND project 225–35 risks 375–6 staff 185–9, 197, 217–18, 253–66 testers 188–9 and traders 258 384 infrastructure, IT 186 instantaneous risk measures 138 insurance 30–1, 50 integration testing 300 interbank systems 158 interbank trading (LIBOR) 39 interest rates 21–3, 33–40 base rate 35 credit effects 39 deltas 175 deposits 35 discount curve 38–9 forward rate agreement 37–8 futures 35–6, 311–12 market participants 34–5 option valuation 67 products 35–8 quantitative analysts 274 swaps 23–5, 36–7 time value of money 33–4 tradeflow issues 39–40 vegas 175 interim delivery of projects 259 internal audit 191–2 International Swaps and Derivatives Association (ISDA) 50 investment banks 9–10 investments, balance sheet item 161 ISDA (International Swaps and Derivatives Association) 50 IT see information technology kappa risk 130 knock in/knock out, barrier options 68 knowledge, risks 359–60 legacy IT systems 282 legal department 189, 293, 316 legal documents 84–5 legal risks 50, 369 leverage 64–6, 76–9 LIBOR (interbank trading) 39 libraries 184–5 lifecycle of a trade see trade lifecycle limit orders 129 limits and credit worthiness 155 imposing 152–3 market risk control 141 line managers 222 INDEX linear derivatives 61, 62 liquidity 73–5, 202, 375 litigation 370 live trading 7 loans 21–3 management see also project management; risk management of changes 109–10 counterparty risk control 157 fees 169 market data usage 317 new products 292 responsibilities of 193–4 risks 374 of teams 229–31 margin payments 156 mark-to-market value 339–40 market data 180, 292, 308–17 business usage 315–17 changes as result of 107–8 curves and surfaces 310–13 sets of 314 market participants 4–5 market risk control 135–45, 190, 363–4 allocation of risk 139 balanced approach 143 controlling the risk 140–1 human factor 143 limitations 142–3 market data usage 316 methodologies 135–9 monitoring of market risk 140 need for risk 139 quantitative analyst role 268 regulatory requirements 143–4 responsibilities 141–2 market sentiment 340 matching of records 94–5 mathematical libraries 238, 241, 270 mathematical models evolution of 343 new products 293 parameters 341 prototypes 238–9 quantitative analyst role 183–5 risks 373 validation team 189–90 maturity of a trade 8, 67, 86, 112–13 MBS see mortgage backed securities 385 Index metal commodities 56, 57 middle office (product control) market data usage 316 new products 293 RABOND project, case study 225–35 role of 180–2 missing data 317 mobile phones 92 models see mathematical models Monte Carlo technique 346–7 mortgage backed securities (MBS) 47 multilateral netting 211–12 NatWest Markets, EcoRisk project 235–47 net present value (NPV) 338–9, 343–8 netting 152, 211–12 new products 289–95 checklist 292–3 evolution of 294 market data 292 market risk control 140 process development/improvement 279–88 risks 194, 294–5, 369 testing 291–2 trial basis for 290–2 new trade types 156 nonlinear derivatives 62–9 nostro accounts 99 NPV see net present value official market data 314 offsetting of risks 128 OIS (overnight indexed swap) 39 operational risks 355–8 operations department 183, 227, 316 operators, IT 188 options 27–30, 61–9 credit default swaps 65–6 exercise 110–12 exotic options 67–9, 109, 235–47, 346 leverage 64–6, 77 risks 127, 357–8 terminology 66 trade process 64–6 valuation 67, 345–6 orders 90–1, 357 OTC see over-the-counter trading OTTC equity confirmation project, case study 247–52 out-of-the-money options 66 over-the-counter (OTC) trading 6–7 clearing 210 commodities 56 price 75 overnight indexed swap (OIS) 39 overnight processes 101–5 P&L see profit and loss parallel testing 301 pay 203, 220–1 payment systems 106–7, 158–60, 357 pension funds 10 people involved in trade lifecycle 177–200 see also working in capital markets back office 183, 227, 316 compliance officers 192–3 conflicts and tensions 196–9, 360–1 control functions 189–99 counterparty risk control department 190–1 finance department 191, 316 human risks 194–9 information technology 185–9, 197, 217–18, 253–66 internal audit 191–2 legal department 189, 293, 316 line managers 222 management 193–4 market risk control department 190 middle office 180–2, 225–35, 293, 316 model validation team 189–90 personality and outlook 194–5, 244–5, 273 programmers 187, 244–5 quantitative analysts 183–5, 267–75 researchers 179–80 revenue generation 177–89 sales department 179, 227, 315, 375 senior managers 126 staffing levels 195 structurers 179 supervisors 204 testers 298–9 traders 125–6, 177–8, 218–23, 226–7, 258, 268, 315, 361 trading assistants 178 trading managers 126, 193 training of staff 193 performance reports 169 personality and outlook 194–5, 244–5, 273 PFE (potential future exposure) 151 physical assets, exercise 111 386 physical commodities, settlement 57–8, 99 planning of processes 282–3 recovery plans 203–4 risks 360 post booking processes 96–7 postal trades 92–3 potential future exposure (PFE) 151 power, abuses of 220, 221–2 pre-execution of a trade 89–91 precious metals 56, 311, 312 premiums 31 price 75–6, 138–9 pricing methods EcoRisk project, case study 235–47 short-term pricing 183 process development/improvement 279–88 coping with change 284 current processes 285–7 evolution of processes 280–1 improving the situation 284–7 inertia 287–8 inventory of current systems 282–4 planning 282–3 timing 288 producers 5 product appetite 4 product control see middle office product development see new products profit curve, commodity trading 54–5 profit and loss (P&L) accounts 164–8 accrued and incidental 165 example 165–6 individual trades 166–7 realised and unrealised 165 responsibility for producing 167 risks associated with reporting 167–8 rogue trading 168 attribution reports 171–6 benefits of 171–2 example 173–6 market movements 173, 175 process 172–3 unexplained differences 173 balance sheet item 163 end of day 182 realised and unrealised 122, 165 programmers 187, 244–5 see also quantitative analysts INDEX project management 225–47, 259, 262 project managers 187–8 proprietary (‘prop’) trading 203 prototypes, IT projects 238–9 provisional trades 89–90, 357–8 put options 62, 63 PVO1, risk measure 138 quality control, IT 260 see also testing quantitative analysts (quants) 183–5, 267–75 and IT professionals 271–4 role of 267–9, 270 seating arrangements 270–1 working methods 269–70 RABOND project, case study 225–35 management 229–31 outcome 233–5 reports 227–9 team management 229–31 traders 226–7 random market data 314 rapid application development (RAD) 260, 281 ratchet options (Cliquet) 68 ratings companies 231–2 raw data 323 raw reporting 331 real world of capital markets see working in capital markets realised P&L 122 receipts 156 reconciliation 121 recovery plans 203–4 recovery rates 52–3, 176 redundancy, processes 282 reform of banks 203 registered securities 123–4 regression testing 302 regulation 201–13, 223–4 authorities 202 Basel II and III 144, 205 credit valuation adjustment 207–13 external 192 internal 224 market risk control 143–4 new products 293 problems 204–5 requirements 202–4 387 Index risk-weighted assets 205–7 risks 369 rehypothication 212–13 remuneration 203, 220–1 reporting currency 42 reports 327–36 accuracy 330–1, 368 calculation process 342 configuration 331–2 consolidated reporting 122 content 328–9 control issues 335 dimensions 333 distribution 329–30, 335, 369 dynamic reports 332–3 end of month reports 182 enhancements 335 errors in 333–4 false reporting 375 financial reports 168–9 frame of reference 333 middle office role 180–1 OTTC equity confirmation project 250, 251 performance reports 169 presentation 329 problems 333–4 profit and loss 167, 171–6 RABOND project 227–9 raw reporting 331 readership 328, 329, 368–9 redundancy of 334–5 requirements 328–33 risks 335–6, 368–70 security issues 335, 368 timing 330 types of 330 reputation, risk to 356, 370 research 268, 375 researchers 179–80 reserve accounts 141 reset date 86 resettable strike, exotic options 68 retail banks 222 revenue generation, people involved in 177–89 rho risk 130 risk 13–16 see also counterparty risk control; market risk control advisory services 370 appetite for 4 business continuity planning 373 calculation process 352 cashflows 124, 367 changes to a trade 110 communication 371 confirmation 95–6, 355 control departments 224 correlation 363 data 324–5, 367–8 definition 13 documentation 356, 374 exercise 112 front office 375–6 human risks 194–9, 359–61 information technology 375–6 instantaneous measures 138 legal risks 369 liquidity 74–5, 375 litigation 370 management risks 374 measures 130, 138, 149–52, 155, 156 model approval 373 new products 140, 194, 294–5, 369 operational risks 355–8 orders 91 origin of risks 126–8 payment systems 357 provisional trades 90, 357–8 quantifying 14 regulation 369 reports 335–6, 368–70 reputation 356, 370 risk-weighted assets 205–7 sales 375 settlement 100, 355–7 short-term thinking 360 straight through processing 357 support activities 376 systematic 202–3, 375–6 testing 304–5, 370–1 types 130–2 unexpected charges 356 unforeseen 16, 353 valuation process 352, 373 risk management 13, 15, 125–34 in absence of trader 128–9 dreaming ahead 131–2 EcoRisk project case study 235–47 hedging strategies 133–4 hedging trades 128 388 risk management (continued) offsetting of risks 128 senior managers 126 traders 125–6, 361 trading managers 126 trading strategies 132 rogue trading 168 sales data 84 sales department 179, 227, 315, 375 SBC Warburg, equity confirmation project, case study 247–52 scenario analysis 136, 198–9, 341 scope creep 187, 264 scrutiny of trades 96 securities, custody of 123–4 security issues 181, 335, 368 semi-static data 309 senior managers 126 sensitivity analysis 138, 347–8 settlement 97–101, 147–8 breaks 101, 356–7 commodities 99 dates 101, 113 nostro accounts 99 quick settlement 101 risks 100, 355–7 shares 44–5 see also equities short selling 65 short-term pricing 183 short-term thinking 195–6, 360 silo approach 257 simple products 70–1 smoke testing 301 sovereign debt 46 speculators 5 spot prices 61, 62, 63, 67, 76–7 spot testing 301 spot trades 18–19, 40, 127 spread of bid/offer 310 spreadsheets 184, 238 staff see people involved in trade lifecycle stale data 105, 318 Standard & Poor’s (S&P) ratings 231–2 static data 309 stop-loss hedging 133–4 stop orders 129 storage of data 309 straight through processing (STP) 93–4, 357 INDEX stress, staff 222, 244–5 stress testing 302 strike price, options 67 structured trades 69–70 structurers 179 supervisors 204 support activities, risks 376 surfaces, market data 310–13 swaps credit default 30–1, 51–2, 65–6, 175, 209 fixings 107 foreign exchange 25, 41 interest rate 23–5, 36–7 yield curves 312–13 swaptions 66 synthetic equities (index) 45 systems see also information technology amalgamation 104–5 analytics 271–2 electronic systems 92 integrated 261 legacy IT systems 282 risks 375–6 testing 251–2, 300 tail behaviour, predicting 143, 364 team management 229–31 telephone transactions 91–2 tensions and conflicts 196–9, 360–1 testing 297–305 back testing 317 boundary testing 351 extreme values 352 fault logging 302–4 importance of 298 mathematical models 239 new products 291–2 risks 304–5, 370–1 stages 300–1 testers 188–9, 298–9 types of 301–2 unit testing 300 user acceptance testing 237, 239–40, 252, 264, 301 when to perform 299–300 theft 355 theta risk 130 time intervals (buckets) 148–9 time lag, commodities 57 389 Index time series analysis 320 timeline of a trade 79, 86–7 trade blotters 93 trade lifecycle 89–115 booking 93–4 business functions 11 changes during lifetime 105–10 confirmation 94–6 equity trades 45 example trade 113–15 execution 91–3 exercise 110–12 maturity 112–13 new products 293 overnight processes 101–5 post booking 96–7 pre execution 89–91 settlement 97–101 trade tickets 102 trade/trading 3–12 see also trade lifecycle anatomy 83–7 business functions 11 complicated trades 340 consequences of 7–8 definition 10–12 financial products 17–31 live trading 7 matching of records 94–5 policies 8 reasons for 3, 9–10 timeline 79, 86–7 transactions 5–7 types 132 tradeflow issues bonds 49 commodities 58 foreign exchange 43–4 interest rates 39–40 traders 177–8, 218–22, 223, 226–7, 258, 268 bonuses 220–1 market data usage 315 risk management 125–6, 361 trading assistants 178 trading desks 70–1, 256–7 trading floor 217–18, 235–6 trading managers 126, 193 training of staff 193 tranche correlation 131 treasury desk 71 trials for new products 290–2 trust 197, 222 UAT see user acceptance testing underlying 83 unexplained differences, P&L reports 173 unforeseen risk 16, 353 unit testing 300 unknown cashflows 345–6 unrealised P&L 122 unwinding a trade, cost of 76 user acceptance testing (UAT) 237, 239–40, 252, 264, 301 validation of models 189–90 valuation process see also calculation process calibration to market 351 mark-to-market value calculation 339–40 middle office role 181 NPV calculation 338–9, 343–8 options 67 problem debugging 242–3 risks 352, 364, 373 valuation systems 269 value at risk (VaR) 136–8, 341 vega (kappa) risk 130 vegas 175 vendors, data services 321 volatility 67, 130 volume of a trade, price effect 76 white box testing 301 workarounds 303 working in capital markets 217–24 see also case studies; people involved in trade lifecycle in 1990s 217–19 culture clashes 219 equal opportunities 219–20 office politics 220–2, 246 positive/negative aspects 222–3 yield curves 312–13 zero bonds 27, 29, 47 Index compiled by Indexing Specialists (UK) Ltd WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley’s ebook EULA.


Solutions Manual - a Primer for the Mathematics of Financial Engineering, Second Edition by Dan Stefanica

asset allocation, Black-Scholes formula, constrained optimization, delta neutral, implied volatility, law of one price, yield curve, zero-coupon bond

口 Problem 15: A five year bond with duration 3~ years is worth 102. Find an approximate price of the bond if the yield decreases by fifty basis points. Solution: Note that , since the yield of the bond decreases , the value of the bond must increase. Recall that the percentage change in the price of the bond can be approximated by the duration of the bond multiplied by the parallel shift in the yield curve , with opposite sign , i.e. , !:1 B 万一句 - !:1 y D For B = 102 , D = 3.5 and !:1 y = -0.005 (since 1% = 100 bp) , we 五nd that !:1B 用 - !:1 y D B = 1. 785. The new value of the bond is Problem 13: If the coupon rate of a bond goes up , what can be said about the value of the bond and its duration? Give a financial argument. Check your answer mathematically, i.e. , by cor叩灿gggand gg7and ShhO 叭仰飞 咄 w ν these 旬 fUl丑 n囚 c ctior丑IS are either always positive or always 丑 1lega 肮,ti忖 ve

Create a butterfly spread by going long a 45-call and a 55-call , and shorting two 50-calls. What are the payoff and the P &L at maturity of the butterfly spread? When would the butterfly spread be profitable? Assume , for simplicity, that interest rates are zero. 4. Dollar duration is defined as D电=一旦 ωθu and measures by how much the value of a bond portfolio changes for a small parallel shift in the yield curve. Similarly, dollar convexity is defined as C虫二工 θ2B eθy2· 58 CHAPTER 2. NUMERICAL INTEGRATION. BONDS. Note that , unlike classical duration and convexity, which can only be computed for individual bonds , dollar duration and dollar convexity can be estimated for any bond portfolio , assuming all bond yields change by the same amount. In particular , for a bond with value B , duration D , and convexity C , the dollar duration and the dollar convexity can be computed as D$ = BD and G$ = BG.


Analysis of Financial Time Series by Ruey S. Tsay

Asian financial crisis, asset allocation, Bayesian statistics, Black-Scholes formula, Brownian motion, business cycle, capital asset pricing model, compound rate of return, correlation coefficient, data acquisition, discrete time, frictionless, frictionless market, implied volatility, index arbitrage, Long Term Capital Management, market microstructure, martingale, p-value, pattern recognition, random walk, risk tolerance, short selling, statistical model, stochastic process, stochastic volatility, telemarketer, transaction costs, value at risk, volatility smile, Wiener process, yield curve

Using the modern econometric terminology, if one assumes that the two interest rate series are unit-root nonstationary, then the behavior of the residuals of Eq. (2.40) indicates that the two interest rates are not co-integrated; see Chapter 8 for discussion of co-integration. In other words, the data fail to support the hypothesis that there exists a long-term equilibrium between the two interest rates. In some sense, this is not surprising because the pattern of “inverted yield curve” did occur during the data span. By inverted yield curve, we mean the situation under which interest rates are inversely related to their time to maturities. 1970 1980 year 1990 2000 ACF 0.0 0.2 0.4 0.6 0.8 1.0 Series : res 0 5 10 15 Lag 20 25 30 Figure 2.14. Residual series of linear regression (2.40) for two U.S. weekly interest rates: (a) time plot, and (b) sample ACF. 69 REGRESSION MODELS WITH TIME SERIES ERRORS -2 per. chg. -1 0 1 2 (a) Change in 1-year rate 1970 1980 year 1990 2000 1990 2000 -2 per. chg. -1 0 1 2 (b) Change in 3-year rate 1970 1980 year Figure 2.15.

Write down the biases and weights of the network in the estimation subsample. • Suppose that we are interested in forecasting the direction of the 1-month ahead stock movement. Fit a 6-5-1 feed-forward neural network to the return series using a Heaviside function for the output node. Compute the 1-step ahead forecasts in the forecasting subsample and compare them with the actual movements. 5. Because of the existence of inverted yield curves in the term structure of interest rates, the spread of interest rates should be nonlinear. To verify this, consider the weekly U.S. interest rates of (a) Treasury 1-year constant maturity rate, and (b) Treasury 3-year constant maturity rate. As in Chapter 2, denote the two interest rates by r1t and r3t , respectively, and the data span is from January 5, 1962 to September 10, 1999. The data are in files “wgs3yr.dat” and “wgs1yr.dat” on the Web. • Let st = r3t − r1t be the spread in log interest rates.

ISBN: 0-471-41544-8 Index ACD model, 197 Exponential, 197 generalized Gamma, 199 threshold, 206 Weibull, 197 Activation function, see Neural network, 147 Airline model, 63 Akaike information criterion (AIC), 37, 315 Arbitrage, 332 ARCH model, 82 estimation, 88 normal, 88 t-distribution, 89 Arranged autoregression, 158 Autocorrelation function (ACF), 24 Autoregressive integrated moving-average (ARIMA) model, 59 Autoregressive model, 29 estimation, 38 forecasting, 39 order, 36 stationarity, 35 Autoregressive moving-average (ARMA) model, 48 forecasting, 53 Back propagation, neural network, 149 Back-shift operator, 33 Bartlett’s formula, 24 Bid-ask bounce, 179 Bid-ask spread, 179 Bilinear model, 128 Black–Scholes, differential equation, 234 Black–Scholes formula European call option, 79, 235 European put option, 236 Brownian motion, 224 geometric, 228 standard, 223 Business cycle, 33 Characteristic equation, 35 Characteristic root, 33, 35 CHARMA model, 107 Cholesky decomposition, 309, 351, 359 Co-integration, 68, 328 Common factor, 383 Companion matrix, 314 Compounding, 3 Conditional distribution, 7 Conditional forecast, 40 Conditional likelihood method, 46 Conjugate prior, see Distribution, 400 Correlation coefficient, 23 constant, 364 time-varying, 370 Cost-of-carry model, 332 Covariance matrix, 300 Cross-correlation matrix, 300, 301 Cross validation, 141 Data 3M stock return, 17, 51, 58, 134 Cisco stock return, 231, 377, 385 Citi-Group stock return, 17 445 446 Data (cont.) equal-weighted index, 17, 45, 46, 73, 129, 160 GE stock return, 434 Hewlett-Packard stock return, 338 Hong Kong market index, 365 IBM stock return, 17, 25, 104, 111, 115, 131, 149, 160, 230, 261, 264, 267, 268, 277, 280, 288, 303, 338, 368, 383, 426 IBM transactions, 182, 184, 188, 192, 203, 210 Intel stock return, 17, 81, 90, 268, 338, 377, 385 Japan market index, 365 Johnson and Johnson’s earning, 61 Mark/Dollar exchange rate, 83 Merrill Lynch stock return, 338 Microsoft stock return, 17 Morgan Stanley Dean Witter stock return, 338 SP 500 excess return, 95, 108 SP 500 index futures, 332, 334 SP 500 index return, 111, 113, 117, 303, 368, 377, 383, 422, 426 SP 500 spot price, 334 U.S. government bond, 19, 305, 347 U.S. interest rate, 19, 66, 408, 416 U.S. real GNP, 33, 136 U.S. unemployment rate, 164 value-weighted index, 17, 25, 37, 73, 103, 160 Data augmentation, 396 Decomposition model, 190 Descriptive statistics, 14 Dickey-Fuller test, 61 Differencing, 60 seasonal, 62 Distribution beta, 402 double exponential, 245 Frechet family, 272 Gamma, 213, 401 generalized error, 103 generalized extreme value, 271 generalized Gamma, 215 generalized Pareto, 291 INDEX inverted chi-squared, 403 multivariate normal, 353, 401 negative binomial, 402 Poisson, 402 posterior, 400 prior, 400 conjugate, 400 Weibull, 214 Diurnal pattern, 181 Donsker’s theorem, 224 Duration between trades, 182 model, 194 Durbin-Watson statistic, 72 EGARCH model, 102 forecasting, 105 Eigenvalue, 350 Eigenvector, 350 EM algorithm, 396 Error-correction model, 331 Estimation, extreme value parameter, 273 Exact likelihood method, 46 Exceedance, 284 Exceeding times, 284 Excess return, 5 Extended autocorrelation function, 51 Extreme value theory, 270 Factor analysis, 342 Factor model, estimation, 343 Factor rotation, varimax, 345 Forecast horizon, 39 origin, 39 Forecasting, MCMC method, 438 Fractional differencing, 72 GARCH model, 93 Cholesky decomposition, 374 multivariate, 363 diagonal, 367 time-varying correlation, 372 GARCH-M model, 101, 431 Geometric ergodicity, 130 Gibbs sampling, 397 Griddy Gibbs, 405 447 INDEX Hazard function, 216 Hh function, 250 Hill estimator, 275 Hyper-parameter, 406 Identifiability, 322 IGARCH model, 100, 259 Implied volatility, 80 Impulse response function, 55 Inverted yield curve, 68 Invertibility, 331 Invertible ARMA model, 55 Ito’s lemma, 228 multivariate, 242 Ito’s process, 226 Joint distribution function, 7 Jump diffusion, 244 Kernel, 139 bandwidth, 140 Epanechnikov, 140 Gaussian, 140 Kernel regression, 139 Kurtosis, 8 excess, 9 Lag operator, 33 Lead-lag relationship, 301 Likelihood function, 14 Linear time series, 27 Liquidity, 179 Ljung–Box statistic, 25, 87 multivariate, 308 Local linear regression, 143 Log return, 4 Logit model, 209 Long-memory stochastic volatility, 111 time series, 72 Long position, 5 Marginal distribution, 7 Markov process, 395 Markov property, 29 Markov switching model, 135, 429 Martingale difference, 93 Maximum likelihood estimate, exact, 320 MCMC method, 146 Mean equation, 82 Mean reversion, 41, 56 Metropolis algorithm, 404 Metropolis–Hasting algorithm, 405 Missing value, 410 Model checking, 39 Moment, of a random variable, 8 Moving-average model, 42 Nadaraya–Watson estimator, 139 Neural network, 146 activation function, 147 feed-forward, 146 skip layer, 148 Neuron, see neural network, 146 Node, see neural network, 146 Nonlinearity test, 152 BDS, 154 bispectral, 153 F-test, 157 Kennan, 156 RESET, 155 Tar-F, 159 Nonstationarity, unit-root, 56 Nonsynchronous trading, 176 Nuisance parameter, 158 Options American, 222 at-the-money, 222 European call, 79 in-the-money, 222 out-of-the-money, 222 stock, 222 strike price, 79, 222 Order statistics, 267 Ordered probit model, 187 Orthogonal factor model, 342 Outlier additive, 410 detection, 413 Parametric bootstrap, 161 Partial autoregressive function (PACF), 36 PCD model, 207 π -weight, 55 Pickands estimator, 275 448 Poisson process, 244 inhomogeneous, 290 intensity function, 286 Portmanteau test, 25.


pages: 1,164 words: 309,327

Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris

active measures, Andrei Shleifer, asset allocation, automated trading system, barriers to entry, Bernie Madoff, business cycle, buttonwood tree, buy and hold, compound rate of return, computerized trading, corporate governance, correlation coefficient, data acquisition, diversified portfolio, fault tolerance, financial innovation, financial intermediation, fixed income, floating exchange rates, High speed trading, index arbitrage, index fund, information asymmetry, information retrieval, interest rate swap, invention of the telegraph, job automation, law of one price, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, market clearing, market design, market fragmentation, market friction, market microstructure, money market fund, Myron Scholes, Nick Leeson, open economy, passive investing, pattern recognition, Ponzi scheme, post-materialism, price discovery process, price discrimination, principal–agent problem, profit motive, race to the bottom, random walk, rent-seeking, risk tolerance, risk-adjusted returns, selection bias, shareholder value, short selling, Small Order Execution System, speech recognition, statistical arbitrage, statistical model, survivorship bias, the market place, transaction costs, two-sided market, winner-take-all economy, yield curve, zero-coupon bond, zero-sum game

Fundamental contract values in a maturity spread differ only with respect to events forecast to occur between the different contract maturity dates. Since such valuation factors make the basis nonstationary, maturity spreads are speculative arbitrages. In practice, the mean-reverting component of the basis usually dominates. The most common maturity spreads are calendar spreads and yield curve spreads. Calendar spreads involve futures contracts or option contracts that mature on different dates. Yield curve spreads involve bonds that mature on different dates. 17.3.2.2 Pairs Trading Pairs traders try to identify pairs of instruments that they believe are mispriced relative to each other. They then buy the one that appears cheap and sell the one that appears expensive. Pairs trading is profitable when the prices of the two instruments have diverged because their common fundamental valuation factors are not consistently priced or because traders have mispriced some instrument-specific factors.

This is a cash-settled futures contract that prices the expiration day value of a standard bond-pricing formula for a hypothetical fixed-rate bond. The hypothetical bond consists of a series of notional fixed 6 percent interest payments followed by the return of the notional principal at the maturity of the hypothetical bond. The pricing formula uses discount rates that are derived from the swaps yield curve, which is computed from ISDA Benchmark Euribor Swap Rate fixings. The Swapnote futures contracts thus derive their values from prices in the swaps market. LIFFE also trades options on Swapnote futures. The Swapnote futures option is a derivative on a derivative on a derivative. (It is an option contract on a futures contract based on swaps contract prices.) ◀ Source: www.liffe.com * * * Swaptions are options on a swap contract.

Most attract little interest and ultimately fail. The most successful recent contract introductions have involved energy and financial products. Some interesting failures have included contracts in sunflower seeds, wool, butter, eggs, high fructose corn syrup, boneless beef trimmings, frozen turkeys, crop yields, barge freight rates, anhydrous ammonia fertilizer, diammonium phosphate fertilizer, various Brady bonds, various yield curve spreads, the U.S. inflation rate, various catastrophe insurance indexes, aluminum, and U.S. silver coins. Table 8-2 lists some recent successful futures contract introductions. TABLE 8-1. Examples of Successful Hedging Markets 8.1.4 Gamblers Gamblers bet on future events. Their bets are contracts whose values depend on the uncertain outcomes of future events. Gamblers commonly bet on sporting events, horse races, lotteries, and card games.


pages: 225 words: 11,355

Financial Market Meltdown: Everything You Need to Know to Understand and Survive the Global Credit Crisis by Kevin Mellyn

asset-backed security, bank run, banking crisis, Bernie Madoff, bonus culture, Bretton Woods, business cycle, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, disintermediation, diversification, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Francis Fukuyama: the end of history, George Santayana, global reserve currency, Home mortgage interest deduction, Isaac Newton, joint-stock company, Kickstarter, liquidity trap, London Interbank Offered Rate, long peace, margin call, market clearing, mass immigration, money market fund, moral hazard, mortgage tax deduction, Northern Rock, offshore financial centre, paradox of thrift, pattern recognition, pension reform, pets.com, plutocrats, Plutocrats, Ponzi scheme, profit maximization, pushing on a string, reserve currency, risk tolerance, risk-adjusted returns, road to serfdom, Ronald Reagan, shareholder value, Silicon Valley, South Sea Bubble, statistical model, The Great Moderation, the new new thing, the payments system, too big to fail, value at risk, very high income, War on Poverty, Y2K, yield curve

All other borrowers going to the bond market should, for any given tenor, have to pay bond buyers more for their money than the risk-free government rate. How much more depends on their specific credit rating but most critically on current market sentiment about risk in general. The effective rate that the government has to pay to borrow money for any given tenor can be plotted as a line called the ‘‘yield curve.’’ Normally, the curve should run from left to right, with rates going up with tenor. However, at times, we can have what is called an ‘‘inverted’’ yield curve, where shorter rates are higher than longer rates. This is because bond rates are set by the market, which is to say by you and me. Although you can buy government debt directly from the treasury, very few people do so. However, all institutional investors, from pension funds to banks and insurance companies are big buyers of government bonds.


pages: 276 words: 82,603

Birth of the Euro by Otmar Issing

"Robert Solow", accounting loophole / creative accounting, Bretton Woods, business climate, business cycle, capital controls, central bank independence, currency peg, financial innovation, floating exchange rates, full employment, inflation targeting, information asymmetry, labour market flexibility, labour mobility, market fundamentalism, money market fund, moral hazard, oil shock, open economy, price anchoring, price stability, purchasing power parity, reserve currency, Y2K, yield curve

In the short to medium term, prices are determined by non-monetary factors such as wages (unit labour costs), the exchange rate, energy and import prices, indirect taxes, etc. Indicators of developments in the real economy include data on employment and unemployment, data from surveys (such as the Ifo Business Climate Index), incoming orders, and so on. This economic analysis also encompasses financial sector data such as the yield curve, stock prices and real estate prices. Asset price trends can yield information, for example, on how the wealth effect is expected to influence the growth of demand of private households. As part of its economic analysis, the ECB takes a broad look at developments in macroeconomic demand and its structure, in costs and in the labour market. This includes taking account of the influence of fiscal policy (spending and revenue) and of external factors (the international economic environment, exports and imports).

Meltzer, ‘A theory of ambiguity, credibility, and inflation under discretion and asymmetric information’, Econometrica, 54:5 (1986). 166 • The ECB – monetary policy for a stable euro only control the (very) short end of the interest rate spectrum. The influence of monetary policy on the long end depends very largely on the markets’ expectations of the central bank’s policy actions in the future and of future inflation. If the mandate is price stability or low inflation, the evolution of interest rates all along the yield curve, and in addition the decisions of agents in virtually all markets, will hinge on how far the latter expect the central bank to fulfil its mandate. Efficient and effective communication can play a major part in influencing expectations in line with the central bank’s policy. In guiding expectations in the financial markets, two dimensions need to be distinguished. On the one hand, short-term indications can be given in advance of policy decisions.


pages: 444 words: 86,565

Investment Banking: Valuation, Leveraged Buyouts, and Mergers and Acquisitions by Joshua Rosenbaum, Joshua Pearl, Joseph R. Perella

asset allocation, asset-backed security, bank run, barriers to entry, business cycle, capital asset pricing model, collateralized debt obligation, corporate governance, credit crunch, discounted cash flows, diversification, fixed income, intangible asset, London Interbank Offered Rate, performance metric, shareholder value, sovereign wealth fund, stocks for the long run, technology bubble, time value of money, transaction costs, yield curve

T-notes are issued with maturities of between one and ten years, while T-bonds are issued with maturities of more than ten years. 89 Yields on nominal Treasury securities at “constant maturity” are interpolated by the U.S. Treasury from the daily yield curve for non-inflation-indexed Treasury securities. This curve, which relates the yield on a security to its time-to-maturity, is based on the closing market bid yields on actively traded Treasury securities in the over-the-counter market. 90 Bloomberg function: “ICUR {# years} <GO>.” For example, the interpolated yield for a 10-year Treasury note can be obtained from Bloomberg by typing “ICUR10,” then pressing <GO>. 91 Located under “Daily Treasury Yield Curve Rates.” 92 The 30-year Treasury bond was discontinued on February 18, 2002, and reintroduced on February 9, 2006. 93 Morningstar acquired Ibbotson Associates in March 2006.


pages: 290 words: 83,248

The Greed Merchants: How the Investment Banks Exploited the System by Philip Augar

Andy Kessler, barriers to entry, Berlin Wall, Big bang: deregulation of the City of London, Bonfire of the Vanities, business cycle, buttonwood tree, buy and hold, capital asset pricing model, commoditize, corporate governance, corporate raider, crony capitalism, cross-subsidies, financial deregulation, financial innovation, fixed income, Gordon Gekko, high net worth, information retrieval, interest rate derivative, invisible hand, John Meriwether, Long Term Capital Management, Martin Wolf, new economy, Nick Leeson, offshore financial centre, pensions crisis, regulatory arbitrage, Sand Hill Road, shareholder value, short selling, Silicon Valley, South Sea Bubble, statistical model, Telecommunications Act of 1996, The Chicago School, The Predators' Ball, The Wealth of Nations by Adam Smith, transaction costs, tulip mania, value at risk, yield curve

Specialists exploit that advantage too: in late 2001, they were accounting for about 32 per cent of all the shares traded.’17 When market makers pre-position their books, the boundaries between customer facilitation, hedging and proprietary trading are fluid and difficult to define. At what point does loading up ahead of expected demand move from being client facilitation to taking a view on the firm’s own account? When does leveraging up to play the yield curve because you know that’s what your customers will do become a proprietary trade? No outsider, and perhaps no insider either, can really tell, making it very difficult to divide trading profits into customer and proprietary. A survey of a group of banks operating in London in 2003 found that several made no distinction in their management accounts between client and proprietary trading.18 It was either an unimportant distinction or, more likely, it was just too difficult to separate out.

It was all about getting volume; now it’s all about margin, making sure there is one.’17 When equity revenues dried up the investment banks quickly switched to fixed income, which grew from being a third of profits in 2000 to two-thirds in 2002. They were helped by favourable market conditions. As interest rates fell to their lowest levels since the 1960s, corporate treasurers rushed to borrow money and to refinance debt at low interest rates. The investment banks were there in a flash, pitching new bond and bond derivatives issues and selling them to fund managers. The yield curve was steep and the proprietary trading departments were able to borrow short, invest long and pick up a huge interest carry. Fixed income people, out of the limelight during the equities bull market, suddenly found themselves the flavour of the month and gained in power, influence and compensation: ‘Bond traders who not long ago were considered second class citizens by their colleagues in investment banking and equities were now back on top of the social pile.’18 The growth of the hedge fund industry also illustrates the investment banks’ ability to latch on to new trends and work up a business around them.


pages: 302 words: 84,428

Mastering the Market Cycle: Getting the Odds on Your Side by Howard Marks

activist fund / activist shareholder / activist investor, Albert Einstein, business cycle, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, financial innovation, fixed income, if you build it, they will come, income inequality, Isaac Newton, job automation, Long Term Capital Management, margin call, money market fund, moral hazard, new economy, profit motive, quantitative easing, race to the bottom, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, secular stagnation, short selling, South Sea Bubble, stocks for the long run, superstar cities, The Chicago School, The Great Moderation, transaction costs, VA Linux, Y2K, yield curve

Here’s how I described the creation of the investment environment in “Risk and Return Today” in October 2004: I’ll use a “typical” market of a few years back to illustrate how this works in real life: The interest rate on the 30-day T-bill might have been 4%. So an investor says, “If I’m going to go out five years, I want 5%. And to buy the 10-year note I have to get 6%.” He demands a higher rate to extend maturity because he’s concerned about the risk to purchasing power, a risk that is assumed to increase with time to maturity. That’s why the yield curve, which in reality is a portion of the capital market line, normally slopes upward with the increase in asset life. Now let’s factor in credit risk. “If the 10-year Treasury pays 6%, I’m not going to buy a 10-year single-A corporate unless I’m promised 7%.” This introduces the concept of credit spreads. Our hypothetical investor wants 100 basis points to go from a “guvvie” to a “corporate.” If the consensus of investors feels the same, that’s what the spread will be.

The meteoric growth of MBS packaging created rapidly increasing demand for the essential raw material in the process: newly issued mortgages. In order to expand the volume of mortgages they issued, lenders hit on novel ways to increase their appeal to borrowers: interest-only mortgages that minimized monthly payments by eliminating the traditional requirement that the principal balance be paid down; adjustable-rate mortgages that allowed borrowers to benefit from the ultra-low interest rates at the short end of the yield curve; and, most importantly, “sub-prime” mortgages (sometimes called “liar loans”) that didn’t require applicants to document income and employment. With sub-prime mortgages being packaged into securities and sold onward, as opposed to being retained as in the past, lenders’ emphasis shifted from borrowers’ creditworthiness to loan volume. With lenders being paid fees simply for making loans—and able to sell them off immediately and thus retain no risk of default—there was no reason for them to worry about the creditworthiness of their borrowers.


pages: 297 words: 91,141

Market Sense and Nonsense by Jack D. Schwager

3Com Palm IPO, asset allocation, Bernie Madoff, Brownian motion, buy and hold, collateralized debt obligation, commodity trading advisor, computerized trading, conceptual framework, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, fixed income, high net worth, implied volatility, index arbitrage, index fund, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, negative equity, pattern recognition, performance metric, pets.com, Ponzi scheme, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, transaction costs, two-sided market, value at risk, yield curve

This strategy seeks to profit from perceived mispricings between different interest rate instruments. Positions are balanced to maintain neutrality to changes in the broad interest rate level, but may express directional biases in terms of the yield curve—anticipated changes in the yield relationship between short-term, medium-term, and long-term interest rates. As an example of a fixed income arbitrage trade, if five-year rates were viewed as being relatively low versus both shorter- and longer-term rates, the portfolio manager might initiate a three-legged trade of long two-year Treasury notes, short five-year T-notes, and long 10-year T-notes, with the position balanced so that it was neutral to parallel shifts in the yield curve. Fixed income arbitrage normally requires the use of substantial leverage because the relative price aberrations it seeks to exploit tend to be small. Therefore, although the magnitude of potential adverse price moves in fixed income arbitrage trades is normally small, the fact that these trades tend to be heavily leveraged can lead to occasional large losses.


Design Patterns: Elements of Reusable Object-Oriented Software (Joanne Romanovich's Library) by Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides

A Pattern Language, Donald Knuth, finite state, loose coupling, MVC pattern, yield curve

In the RTL System for compiler code optimization [JML92], strategies define different register allocation schemes (RegisterAllocator) and instruction set scheduling policies (RISCscheduler, CISCscheduler). This provides flexibility in targeting the optimizer for different machine architectures. The ET++SwapsManager calculation engine framework computes prices for different financial instruments [EG92]. Its key abstractions are Instrument and Yield-Curve. Different instruments are implemented as subclasses of Instrument. Yield-Curve calculates discount factors, which determine the present value of future cash flows. Both of these classes delegate some behavior to Strategy objects. The framework provides a family of ConcreteStrategy classes for generating cash flows, valuing swaps, and calculating discount factors. You can create new calculation engines by configuring Instrument and YieldCurve with the different ConcreteStrategy objects.


pages: 337 words: 89,075

Understanding Asset Allocation: An Intuitive Approach to Maximizing Your Portfolio by Victor A. Canto

accounting loophole / creative accounting, airline deregulation, Andrei Shleifer, asset allocation, Bretton Woods, business cycle, buy and hold, buy low sell high, capital asset pricing model, commodity trading advisor, corporate governance, discounted cash flows, diversification, diversified portfolio, fixed income, frictionless, high net worth, index fund, inflation targeting, invisible hand, John Meriwether, law of one price, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low cost airline, market bubble, merger arbitrage, money market fund, new economy, passive investing, Paul Samuelson, price mechanism, purchasing power parity, risk tolerance, risk-adjusted returns, risk/return, Ronald Reagan, selection bias, shareholder value, Sharpe ratio, short selling, statistical arbitrage, stocks for the long run, survivorship bias, the market place, transaction costs, Y2K, yield curve, zero-sum game

This is what Victor eloquently maps out in the pages ahead. The top-down forces within an economy are many. At the very top are those that directly arise from government policy, such as taxes, money supply, and regulations. Moving down a notch, there’s the value of the dollar, foreign exchange rates, and trade balances. As noted, there’s inflation and the inflation indicators, such as gold prices and Treasury yield curves that speak to the phenomenon of the way money and goods interact. On the corporate level, there’s inventory, shipments, and retained earnings. After that, there’s employment, productivity, and wage levels. Then there’s the abstract, such as supply and demand curves, or the elasticities inherent in different industries and businesses. The list is long, and highly interrelated, which can be problematic for investors at any level of expertise.

The decline in asset prices also reduced the net capital and capital adequacy of the banks, forcing them to further curtail their loan operations. These conditions created what some called a liquidity trap. As the Japan central bank printed money to stimulate the economy, the commercial banks did not lend the extra money. Instead, the money was held as excess reserves. The abundance of bank reserves reduced short-term interest rates, while stagnation lowered long-term rates. Worse, the yield curve flattened to near zero levels, hence the liquidity trap. The Japanese economy remained stagnant for several years following this turn of events. Eventually, most of the bad loans were worked out and the banks began lending again, once their capital had increased. Rising asset prices started to generate a virtuous cycle, and climbing net worth in the Japanese private sector made the sector’s credit worthy once more.


pages: 361 words: 97,787

The Curse of Cash by Kenneth S Rogoff

Andrei Shleifer, Asian financial crisis, bank run, Ben Bernanke: helicopter money, Berlin Wall, bitcoin, blockchain, Boris Johnson, Bretton Woods, business cycle, capital controls, Carmen Reinhart, cashless society, central bank independence, cryptocurrency, debt deflation, disruptive innovation, distributed ledger, Edward Snowden, Ethereum, ethereum blockchain, eurozone crisis, Fall of the Berlin Wall, fiat currency, financial exclusion, financial intermediation, financial repression, forward guidance, frictionless, full employment, George Akerlof, German hyperinflation, illegal immigration, inflation targeting, informal economy, interest rate swap, Isaac Newton, Johann Wolfgang von Goethe, Johannes Kepler, Kenneth Rogoff, labor-force participation, large denomination, liquidity trap, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, moveable type in China, New Economic Geography, offshore financial centre, oil shock, open economy, payday loans, price stability, purchasing power parity, quantitative easing, RAND corporation, RFID, savings glut, secular stagnation, seigniorage, The Great Moderation, the payments system, The Rise and Fall of American Growth, transaction costs, unbanked and underbanked, unconventional monetary instruments, underbanked, unorthodox policies, Y2K, yield curve

Clearly, it would be helpful to have legal and financial experts examine every aspect of negative rates to make a transition as smooth as possible. Finally, when thinking about these hurdles, it is again important to bear in mind that part of the idea of employing negative short-term policy rates is to raise current and future expected inflation, thereby raising long-term rates and tilting the yield curve up. Even if short rates were expected to remain negative for a year or even two, one would not expect long-term nominal rates to be negative if the central bank seems determined to create inflation. Admittedly, it is difficult to know how aggressively the central bank will need to move to dislodge deflationary expectations. Especially when negative rates are a new tool, an overshoot may be necessary, but with such a powerful instrument, the central bank should be able to move the dial on expectations pretty quickly.

Chung et al. (2012), for example, argue that unemployment in the United States at the end of 2012 would have been 1.5% higher in the absence of QE, a very powerful effect. They also find, however, that most of this came from QE during the height of the crisis and not later rounds. Wu and Xia (2016) suggest that the effects found in studies such as Chung et al. (2012) may overstate the effect of QE, because it is implicitly assumed that there is a large effect across the yield curve. 29. Krishnamurthy and Vissing-Jorgensen (2011, 2013). 30. Professor James Hamilton of the University of San Diego, whose work spans both macroeconomics and econometrics, gives an extremely insightful discussion on the difficulty of discerning any long-term effect of QE in his Econbrowser column “Evaluation of Quantitative Easing,” November 2, 2014, available at http://econbrowser.com/archives/2014/11/evaluation-of-quantitative-easing. 31.


pages: 354 words: 26,550

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems by Irene Aldridge

algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business cycle, business process, buy and hold, capital asset pricing model, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, computerized trading, diversification, equity premium, fault tolerance, financial intermediation, fixed income, high net worth, implied volatility, index arbitrage, information asymmetry, interest rate swap, inventory management, law of one price, Long Term Capital Management, Louis Bachelier, margin call, market friction, market microstructure, martingale, Myron Scholes, New Journalism, p-value, paper trading, performance metric, profit motive, purchasing power parity, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, statistical model, stochastic process, stochastic volatility, systematic trading, trade route, transaction costs, value at risk, yield curve, zero-sum game

Fleming and Remolona (1999) estimate the high-frequency impact of macroeconomic announcements on the entire U.S. Treasury yield curve. Fleming and Remolona (1999) measure the impact of 10 distinct announcement classes: consumer price index (CPI), durable goods orders, gross domestic product (GDP), housing starts, jobless rate, leading indicators, non-farm payrolls, producer price index (PPI), retail sales, and trade balance. Fleming and Remolona (1999) define the macroeconomic surprise to be the actual number released less the Thomson Reuters consensus forecast for the same news release. All of the 10 macroeconomic news announcements studied by Fleming and Remolona (1999) were released at 8:30 A . M. The authors then measure the significance of the impact of the news releases on the entire yield curve from 8:30 A . M. to 8:35 A . M., and document statistically significant average changes in yields in response to a 1 percent positive surprise change in the macro variable.


pages: 368 words: 32,950

How the City Really Works: The Definitive Guide to Money and Investing in London's Square Mile by Alexander Davidson

accounting loophole / creative accounting, algorithmic trading, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, Big bang: deregulation of the City of London, buy and hold, capital asset pricing model, central bank independence, corporate governance, Credit Default Swap, dematerialisation, discounted cash flows, diversified portfolio, double entry bookkeeping, Edward Lloyd's coffeehouse, Elliott wave, Exxon Valdez, forensic accounting, global reserve currency, high net worth, index fund, inflation targeting, intangible asset, interest rate derivative, interest rate swap, John Meriwether, London Interbank Offered Rate, Long Term Capital Management, margin call, market fundamentalism, Nick Leeson, North Sea oil, Northern Rock, pension reform, Piper Alpha, price stability, purchasing power parity, Real Time Gross Settlement, reserve currency, Right to Buy, shareholder value, short selling, The Wealth of Nations by Adam Smith, transaction costs, value at risk, yield curve, zero-coupon bond

The Debt Management Office (DMO), established in April 1998 as an executive agency of HM Treasury, sells one-month and three-month treasury bills (T-bills) on the government’s behalf every Friday in a tender offer to banks, and six-month bills about once a month. The T-bill is issued in sterling at a discount to face value, and the face value is later repaid, the difference being interest equivalent. The T-bill is traded less than it was because developed countries such as the UK and United States are able to borrow for longer periods, which is cheaper based on the inverted yield curve that reflects a decline in bond yields as the maturity extends into the future. Issuance is consequently more likely in bonds than in T-bills. The euro bill is similar to the T-bill but is issued in euros. The Bank of England issues £900 million a month in three and six-month euro bills, which helps it to fund euro liabilities. ______________________________________ INTEREST RATE PRODUCTS 83  The certificate of deposit (CD) is a money market instrument distinguished by its maturity date and its fixed interest rate.

But the closer bonds are to maturity, the less influence this will have in comparison with the pull to par. On this basis, long-term bonds, particularly if undated, are more exposed to interest rates because redemption is further off. If you think interest rates will go down, you should buy long-term bonds. The yields are generally higher to compensate for a perceived greater risk, despite the inverted yield curve discussed earlier. A bond may often be callable, which means that the issuer, usually a company, may redeem it before maturity. If interest rates should decline, the issuer is likely to call the bond and reissue it at a lower rate of interest. The investor would then be left with money to reinvest in a world where interest rates are low. To compensate for this reinvestment risk, the callable bond will often pay a high coupon. 12 Credit products Introduction In this chapter, we will cover credit products, as distinct from the interest rate products covered in Chapter 11.


Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals by David Aronson

Albert Einstein, Andrew Wiles, asset allocation, availability heuristic, backtesting, Black Swan, butter production in bangladesh, buy and hold, capital asset pricing model, cognitive dissonance, compound rate of return, computerized trading, Daniel Kahneman / Amos Tversky, distributed generation, Elliott wave, en.wikipedia.org, feminist movement, hindsight bias, index fund, invention of the telescope, invisible hand, Long Term Capital Management, mental accounting, meta analysis, meta-analysis, p-value, pattern recognition, Paul Samuelson, Ponzi scheme, price anchoring, price stability, quantitative trading / quantitative finance, Ralph Nelson Elliott, random walk, retrograde motion, revision control, risk tolerance, risk-adjusted returns, riskless arbitrage, Robert Shiller, Robert Shiller, Sharpe ratio, short selling, source of truth, statistical model, stocks for the long run, systematic trading, the scientific method, transfer pricing, unbiased observer, yield curve, Yogi Berra

This transformation was used in the case study and was performed on four interest rate series: three-month treasury bills, 10-year treasury bonds, Moody’s AAA corporate bonds, and Moody’s BAA corporate bonds. Interest Rate Spreads. An interest-rate spread is the difference between two comparable interest rates. Two types of interest-rate spreads were constructed for the case study; the duration spread and the quality spread. The duration spread, also known as the slope of the yield curve, is the difference between yields on debt instruments having the same credit quality but having different durations (i.e., time to maturity). The duration spread used in the case study was defined as the yield on the 10-year treasury note minus the yield on the three-month treasury bills (10-year yield minus 3-month yield). The spread was defined in this way rather than 3-month minus 10-year so that an upward trend in the spread would presumably have bullish implications for the stock market (S&P 500).37 A quality spread measures the difference in yield between instruments with similar durations but with different credit qualities (default risk).

Ross, “The Arbitrage Theory of Capital Asset Pricing,” Journal of Economic Theory (December 1976). APT says a linear Notes 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 503 model relates a stock’s returns to a number of factors, and this relationship is the result of arbitrageurs hunting for riskless, zero-investment opportunities. APT specifies factors, including the rate of inflation, the spread between low and high quality bonds, the slope of the yield curve, and so on. In other words, the current price change is correlated with the prior change (i.e., a lag interval of 1), then with the change prior to that (lag interval = 2), and so forth. A correlation coefficient is computed for each of these lags. Typically, in a random data series, the correlations drop off very quickly as a function of the lag interval. However, if the time series of price changes is nonrandom in a linear fashion, some of the autocorrelation coefficients will differ significantly from the pattern of autocorrelations of a random sequence.

., 467 Scientific method: defined, 103, 332 history of, 103–108 hypothetic-deductive method, 144–147 key aspects of, 147–148 logic and, 111–124 INDEX nature of scientific knowledge and, 108–110 objectification of subjective technical analysis, 148–151 example, 151–161 openness and skepticism in, 143, 225 philosophy of, 124–143 search bias and, 64 Secondhand information bias, 58–61 anchoring and, 360–361 information diffusion and, 365–366 Self-attribution bias, 48–49 DHS hypothesis and, 375–376 Self-interest, secondhand accounts and, 61 Shermer, Michael, 38 Shiller, Robert, 333–334, 365, 366 Shleifer, Andre, 347 Siegel, Jeremy, 84 Signals, 16–18 Simon, Barry, 259 Simon, Herbert, 42 Simplicity, principle of, 107–108, 225–227 Single-rule back-testing, versus data mining, 268–271 Skepticism, 143, 225 Slope of yield curve, 417 Slovic, Paul, 41, 470 Snelson, Jay Stuart, 71 Socioeconomics, 151 Spatial clustering, 100–101 Stale information, 340, 349, 351–354 Standard deviation, 192 Standard error of the mean, 213–215 Statement about reliability of inference, 190 Index Stationary statistical problems, 174, 188 Stationary time series, 19 Statistical analysis: descriptive statistics tools: central tendency measurements, 191 frequency distribution, 190–191 variability (dispersion) measurements, 192–193 inferential statistics: elements of statistical inference problem, 186–190 sampling example, 172–186 three distributions of, 206–207 probability, 193 Law of Large Numbers, 194–195 probability distribution, 200–202 probability distribution of random variables, 197–199 theoretical versus empirical, 196 sampling distribution and, 201–206 classical derivation approach, 209–215 computer-intensive approach, 215 used to counter uncertainty, 165–172 Statistical hypothesis, defined, 220 Statistical inference: data mining and, 272–278 defined, 189 hypothesis tests: computer-intensive methods of sampling distribution generation, 234–243 confidence intervals contrasted to, 250–252 527 defined, 217–218 informal inference contrasted, 218–223 mechanics of, 227–234 rationale of, 223–227 parameter estimation: defined, 217–218 interval estimates, 218, 243, 245–253 point estimates, 218, 243–245 Statistical significance, 23 in case study, 394 statistical significance of observation, 171 statistical significance of test (p-value), 232–234 Stiglitz, J.E., 343, 378 Stochastics, 401–403 Stories, see Secondhand information bias Subjective technical analysis, 5–8, 15–16, 161–163 adoption of scientific method and, 148–151 example, 151–161 chart analysis and, 82–86 confirmation bias and, 62–71 erroneous beliefs and, 33–35 futility of forecasting and, 465–471 heuristic bias and, 86–93 illusion trends and chart patterns, 93–101 human pattern finding and information processing, 39–45 illusory correlations and, 72–82 overconfidence bias and, 45–58 secondhand information bias and, 58–61 as untestable and not legitimate knowledge, 35–38 528 Syllogisms: categorical, 112–115 conditional, 115–116 invalid forms, 118–121 valid forms, 117–118 Taleb, Nassim, 337 Technical analysis (TA), 9–11.


Money and Government: The Past and Future of Economics by Robert Skidelsky

anti-globalists, Asian financial crisis, asset-backed security, bank run, banking crisis, banks create money, barriers to entry, Basel III, basic income, Ben Bernanke: helicopter money, Big bang: deregulation of the City of London, Bretton Woods, British Empire, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, constrained optimization, Corn Laws, correlation does not imply causation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, David Graeber, David Ricardo: comparative advantage, debt deflation, Deng Xiaoping, Donald Trump, Eugene Fama: efficient market hypothesis, eurozone crisis, financial deregulation, financial innovation, Financial Instability Hypothesis, forward guidance, Fractional reserve banking, full employment, Gini coefficient, Growth in a Time of Debt, Hyman Minsky, income inequality, incomplete markets, inflation targeting, invisible hand, Isaac Newton, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kenneth Rogoff, labour market flexibility, labour mobility, law of one price, liberal capitalism, light touch regulation, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, market clearing, market friction, Martin Wolf, means of production, Mexican peso crisis / tequila crisis, mobile money, Mont Pelerin Society, moral hazard, mortgage debt, new economy, Nick Leeson, North Sea oil, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, Pareto efficiency, Paul Samuelson, placebo effect, price stability, profit maximization, quantitative easing, random walk, regulatory arbitrage, rent-seeking, reserve currency, Richard Thaler, rising living standards, risk/return, road to serfdom, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, secular stagnation, shareholder value, short selling, Simon Kuznets, structural adjustment programs, The Chicago School, The Great Moderation, the payments system, The Wealth of Nations by Adam Smith, Thomas Malthus, Thorstein Veblen, too big to fail, trade liberalization, value at risk, Washington Consensus, yield curve, zero-sum game

He now doubted the ability of the monetary authority to get interest rates low enough and prices high enough to offset a marked rise in liquidity preference. However, there was a role for monetary policy in ‘normal’ times, which was to maintain continuously low long-term interest rates. For this reason, Keynes opposed the use of ‘dear money’ to check a boom. The effect of a rise in the interest rate on the yield curve would be very difficult to reverse. A low enough long-term rate of interest cannot be achieved if we allow it to be believed that better terms will be obtainable from time to time by those who keep their resources liquid. The long-term rate of interest must be kept continuously as near as possible to what we believe 124 k e y n e s’s i n t e rv e n t ion to be the long-term optimum. It is not suitable to be used as a shortperiod weapon.64 In arguing for the continued (if now limited) power of monetary policy over interest rates, Keynes reverted to the ideas of the Tract on Monetary Reform and A Treatise on Money.

The MPC’s preferred measure for this was broad money (M4), which included bank deposits. In addition, the Bank retained its traditional role as lender of last resort, a role denied to the European Central Bank. 249 M ac roe c onom ic s i n t h e C r a s h a n d A f t e r , 2 0 0 7 – Bank Rate, less familiarly the ‘base rate’, is the interest rate or ‘price’ that the central bank charges for lending money to member banks. The theory is that a change in the base rate pushes the yield curve upwards or downwards. It is immediately transmitted to the interbank lending rate. Banks will then adjust their own lending rates, both short-term and long-term. This will affect how much income is saved and invested. In 1930 the Bank of England had denied that it had such power over commercial lending rates, and uncertainty remained about the impact of the short-rate on the long-rate.9 The supposed transmission mechanism from the base rate to the level of spending and prices in the economy can be summarized by Figure 38.

UK exchange and QE 105 100 current account deficit exchange rate QE 8 7 6 95 5 90 4 85 3 80 2 75 1 70 0 267 Current account deficit (per cent of GDP) Effective exchange rate, sterling (indexed to 100 in Jan-05) Fe b0 Au 6 g-0 Fe 6 b0 Au 7 g-0 Fe 7 b0 Au 8 g-0 Fe 8 b0 Au 9 g-0 Fe 9 b1 Au 0 g-1 Fe 0 b1 Au 1 g-1 Fe 1 b1 Au 2 g-1 Fe 2 b1 Au 3 g-1 Fe 3 b1 Au 4 g-1 Fe 4 b1 Au 5 g-1 Fe 5 b16 110 M ac roe c onom ic s i n t h e C r a s h a n d A f t e r , 2 0 0 7 – monetary policy, became increasingly acute in guessing the size and timing of the next wave. As a consequence, QE2 had much less impact than QE1. However, central banks played the strategic game. By announcing changes in the composition of purchases, like the Fed’s ‘Operation Twist’ and the Bank of England’s decision to ‘increase the amount of shorter dated securities’, they were able to surprise investors and continue, at least in their own view, to make impacts on yield curves.61 Through the four channels above, the injection of narrow money (M1) was supposed to influence the movement of broad money and, through broad money, growth in nominal GDP. Broad Money Broad money is largely synonymous with bank lending. As we have seen, bank reserves went up while bank lending fell. The same story can be told with broad money. The presumed relationship between narrow money and broad money (the money multiplier) never emerged, because the decrease in velocity of circulation offset the effect of QE.


pages: 399 words: 114,787

Dark Towers: Deutsche Bank, Donald Trump, and an Epic Trail of Destruction by David Enrich

Affordable Care Act / Obamacare, anti-globalists, Asian financial crisis, banking crisis, Berlin Wall, buy low sell high, collateralized debt obligation, commoditize, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, Donald Trump, East Village, estate planning, Fall of the Berlin Wall, financial innovation, forensic accounting, high net worth, housing crisis, interest rate derivative, interest rate swap, Jeffrey Epstein, London Interbank Offered Rate, Lyft, Mikhail Gorbachev, NetJets, obamacare, offshore financial centre, post-materialism, Ralph Waldo Emerson, Renaissance Technologies, risk tolerance, Robert Mercer, rolodex, sovereign wealth fund, too big to fail, transcontinental railway, yield curve

At the time, the list of derivatives available to Merrill’s clients was limited. Bill and Edson expanded the menu. Bill started dreaming up new types of a popular derivative known as swaps that were designed to help institutions protect themselves from changes in things like interest rates. He combined different types of swaps into mutant instruments with names like callable interest rate swaps and yield curve swaps and swaptions. This was good news for clients and great news for Merrill. Each time Merrill sold a swap to a client, it pocketed a fee. What’s more, Broeksmit devised clever new ways for Merrill to protect itself by using derivatives when it bought assets from customers. Because the derivatives were reducing the risks Merrill faced on various transactions, the firm now had a greater capacity to do more of those transactions—which meant more revenue for Merrill.

See Trump, Donald, presidential campaign of 2016 United States elections of 2018, 353 United States Football League (USFL), 270 United States housing bubble, 134, 137–40, 157–58 University Club, 242, 284–86 University of Massachusetts (UMass), 46, 47, 57 University of Pennsylvania, 126 Ursuline School, 166, 168–69 US Open, 113 Vaccaro, Jon, 78 Vekselberg, Viktor, 338 Venmo, 351 Villard, Henry, 13–15, 16–18, 310 Northern Pacific Railway loan, 13, 14–15, 17–18 Virgin Atlantic, 229 Virgin Gorda, 207–208 Volcker Rule, 343 Vonnegut, Kurt, 68 Vrablic, Rosemary background of, 166–67 at Bank of America, 168–69 at Citicorp, 167–68 Trump presidency and, 320 Vrablic, Rosemary, at Deutsche Bank Kushners and, 169, 172, 275–77, 306–307, 312 Trump Jr. loan, 275 Vrablic, Rosemary, at Deutsche Bank, and Trump loans, 6–7, 172–74, 270–71, 306–308, 354 Doral property, 175–77, 270, 278 McFadden review, 310–11 Washington, D.C. property, 273–75 VTB Bank, 109–10, 197, 317–18, 327, 328–29 Walker, Dick, 93, 93n, 264, 344–45 Wall Street Journal, 41, 49, 64, 255, 323, 329 DBTCA and Federal Reserve story, 241–42, 249–50 Val’s contact with author, 247–48, 252, 257, 279 Washington Monument, 271 Waters, Maxine, 353 Waugh, Seth, 113–14, 116, 119, 187 Wauthier, Pierre, 203–204, 227, 244 Weber, Axel, 180 Weinstein, Boaz, 128, 137 West, Kanye, 178 Wilcox, Fiona, 287–89 Wilhelm, German Emperor, 15, 178 Wimbledon, 220 Wisley Golf Club, 60, 61, 83 Wiswell, Tim, 198–99, 202, 232–33, 236, 329, 358 Wolfe, Tom, 172 World Economic Forum (Davos), 209–10, 261 World War I reparations, 21 World War II, 19–21. See also Nazi Party Xanax, 226, 285, 287 Yale Club (New York City), 334–35 Yallop, Mark, 81 Yield curve swaps, 34 Young, Neil, 245 Young, Pegi, 244–45, 248, 279, 353, 354–55 Zurich Insurance Group, 203–204, 204n, 210, 227, 338 Zyklon B, 20 About the Author DAVID ENRICH is the finance editor at the New York Times. He previously was an editor and reporter at the Wall Street Journal in New York and London. He has won numerous journalism awards, including the 2016 Gerald Loeb Award for feature writing.


pages: 358 words: 119,272

Anatomy of the Bear: Lessons From Wall Street's Four Great Bottoms by Russell Napier

Albert Einstein, asset allocation, banking crisis, Bretton Woods, business cycle, buy and hold, collective bargaining, Columbine, cuban missile crisis, desegregation, diversified portfolio, floating exchange rates, Fractional reserve banking, full employment, hindsight bias, Kickstarter, Long Term Capital Management, market bubble, mortgage tax deduction, Myron Scholes, new economy, oil shock, price stability, reserve currency, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, short selling, stocks for the long run, yield curve, Yogi Berra

In fact, some form of intervention policy was to remain in force until March 1951. Although only the commitment to intervene in the Treasury bill market was explicit, in practice the actions of the Federal Reserve created a capped federal debt yield curve. The rationale behind these actions was that it would encourage investors, who would not be speculating on higher future interest rates, to buy War Bonds and thus reduce the cost of financing the war. The de facto maximum permitted yield on the longest-term government bonds was 2.5%. The capped rates effectively enshrined the positively sloping yield curve, which the market was dictating prior to April 1942, for the duration of the war and beyond. Not surprisingly the investing public and the commercial banks flocked to the long end of the market, where the risk of capital loss had been eliminated for the duration of the war, and ownership of the T-bill market passed increasingly to the Federal Reserve.


pages: 620 words: 214,639

House of Cards: A Tale of Hubris and Wretched Excess on Wall Street by William D. Cohan

asset-backed security, call centre, collateralized debt obligation, corporate governance, corporate raider, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Deng Xiaoping, diversification, Financial Instability Hypothesis, fixed income, Hyman Minsky, Irwin Jacobs, John Meriwether, Long Term Capital Management, margin call, merger arbitrage, money market fund, moral hazard, mortgage debt, mutually assured destruction, Myron Scholes, New Journalism, Northern Rock, Renaissance Technologies, Rod Stewart played at Stephen Schwarzman birthday party, savings glut, shareholder value, sovereign wealth fund, too big to fail, traveling salesman, Y2K, yield curve

Either this strategy was going to work or they weren't going very far at all.” Lewis had to move the war bonds. “This man would be gone for two weeks at a time in every single city and every single bank to get control of their portfolios and move the bonds around,” Sandy Lewis said. “He was known throughout the United States. If he could do this, he could support the rail bonds. This was all about ‘I'll help you. I'll rearrange your portfolio.' He could run a yield curve in his sleep. All so that he could earn enough revenue to support this huge bet that he had made.” Bear Stearns would have been bankrupt if Lewis's bet had failed. “Gone,” Sandy said. “Out of business. No question about it. They weren't an underwriting firm. They weren't a mergers firm. They didn't have any other business.” After the United States and its allies won the war, the victory party played out exactly the way Cy Lewis had hoped.

CAYNE CAPS SPECTOR y 2004, there was no question that the businesses at the firm that reported to Warren Spector—fixed income, institutional equities, and asset management—were driving the growth of the firm. Particularly important was the exponential growth of the fixed-income business, which was without question Spector's fiefdom. Fixed-income revenue in 2004 was $3.1 billion—nearly 45 percent of the firm's overall revenue of $6.8 billion—and had increased some 63 percent, from $1.9 billion in revenues, since 2002. “These businesses benefited from the low level of interest rates, a steep yield curve and narrowing of corporate credit spreads,” the firm reported in its SEC filings. “Mortgage-backed securities revenues increased significantly as residential mortgage refinancing activity reached record levels during the year, driving record new issue activity, and demand for high-quality fixed income investments continued.” The firm did not break out separately the profitability of Spector's fixed-income division, but the overall grouping of investment banking (Schwartz's bailiwick), institutional equities, and fixed income had increased its pretax income to $2 billion in 2004, from $1.3 billion in 2002, and it would be safe to say that much of the increase in the pretax income came from the growth in the fixed-income division.

For the past five years or so, mortgages have provided the best vehicle for asset growth.” While Bear Stearns's fixed-income revenue for the year ended November 30, 2005, declined 12 percent, to $2.3 billion, from $2.6 billion in 2004, the business was still humming along quite profitably. The firm's SEC filing stated that “mortgage-backed securities origination revenues declined from the robust levels of fiscal 2004 due to a flattening yield curve, shifting market conditions and changes in product mix. A decline in agency CMO volumes was offset by an increase in non-agency mortgage originations.” Regardless of the stumble in fixed income, the firm posted a record profit of $1.5 billion in fiscal 2005, up 9 percent from the $1.3 billion of net income the year before. Since the start of 2006, Bear's stock had risen nearly 25 percent, closing at $143.50 on April 17.


pages: 483 words: 141,836

Red-Blooded Risk: The Secret History of Wall Street by Aaron Brown, Eric Kim

activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, Asian financial crisis, Atul Gawande, backtesting, Basel III, Bayesian statistics, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Black Swan, business cycle, capital asset pricing model, central bank independence, Checklist Manifesto, corporate governance, creative destruction, credit crunch, Credit Default Swap, disintermediation, distributed generation, diversification, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, experimental subject, financial innovation, illegal immigration, implied volatility, index fund, Long Term Capital Management, loss aversion, margin call, market clearing, market fundamentalism, market microstructure, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, natural language processing, open economy, Pierre-Simon Laplace, pre–internet, quantitative trading / quantitative finance, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, special drawing rights, statistical arbitrage, stochastic volatility, stocks for the long run, The Myth of the Rational Market, Thomas Bayes, too big to fail, transaction costs, value at risk, yield curve

The reason people solve for the yield to maturity of bonds is that two bonds with similar terms and credit qualities will have similar yields, but not necessarily similar prices. Thus we can take the price of a bond we do know, convert it to a yield, and apply the yield to get the price of a similar bond whose price we do not know. We can graph bond yield versus time to maturity to get a reasonably smooth yield curve. This is both a useful economic indicator and a way to interpolate yields of other bonds. We can also graph bond yield versus credit quality with similar results. And we can take the derivative of bond price with respect to yield to get a first order idea of the volatility of a bond. The Black-Scholes-Merton model works the same way. Two options with similar terms will have similar implied volatilities, but not necessarily similar prices.

Two options with similar terms will have similar implied volatilities, but not necessarily similar prices. So we can use implied volatilities of options we know the prices of to estimate the implied volatilities, and hence the prices, of options whose prices we do not know. We can graph option implied volatility versus time or moneyness (the ratio of the strike price of an option to the underlying price) and get the same kind of insights we get from yield curves and credit curves. We can take the derivative of option price with respect to implied volatility, known as vega, which some forgotten trader thought was a Greek letter. All of this is pure mathematics; it does not require any economic assumptions. Anyway, the derivative concept in the old sense led to a fresh conflation of frequency and degree of belief. If an option price can be mathematically derived from the underlying stock price, then its price obviously does not depend on the utility function of the person evaluating it.


pages: 436 words: 76

Culture and Prosperity: The Truth About Markets - Why Some Nations Are Rich but Most Remain Poor by John Kay

"Robert Solow", Albert Einstein, Asian financial crisis, Barry Marshall: ulcers, Berlin Wall, Big bang: deregulation of the City of London, business cycle, California gold rush, complexity theory, computer age, constrained optimization, corporate governance, corporate social responsibility, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, Donald Trump, double entry bookkeeping, double helix, Edward Lloyd's coffeehouse, equity premium, Ernest Rutherford, European colonialism, experimental economics, Exxon Valdez, failed state, financial innovation, Francis Fukuyama: the end of history, George Akerlof, George Gilder, greed is good, Gunnar Myrdal, haute couture, illegal immigration, income inequality, industrial cluster, information asymmetry, intangible asset, invention of the telephone, invention of the wheel, invisible hand, John Meriwether, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, knowledge economy, light touch regulation, Long Term Capital Management, loss aversion, Mahatma Gandhi, market bubble, market clearing, market fundamentalism, means of production, Menlo Park, Mikhail Gorbachev, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, Naomi Klein, Nash equilibrium, new economy, oil shale / tar sands, oil shock, Pareto efficiency, Paul Samuelson, pets.com, popular electronics, price discrimination, price mechanism, prisoner's dilemma, profit maximization, purchasing power parity, QWERTY keyboard, Ralph Nader, RAND corporation, random walk, rent-seeking, Right to Buy, risk tolerance, road to serfdom, Ronald Coase, Ronald Reagan, second-price auction, shareholder value, Silicon Valley, Simon Kuznets, South Sea Bubble, Steve Jobs, telemarketer, The Chicago School, The Market for Lemons, The Nature of the Firm, the new new thing, The Predators' Ball, The Wealth of Nations by Adam Smith, Thorstein Veblen, total factor productivity, transaction costs, tulip mania, urban decay, Vilfredo Pareto, Washington Consensus, women in the workforce, yield curve, yield management

Places The American Business Model The Future of Economics The Future of Capitalism 277 289 302 311 323 340 Appendix: Nobel Prizes in Economics Glossary Notes Bibliography Index 356 361 365 390 411 17 18 19 20 21 22 23 {part V} 24 25 26 27 28 29 {List of Figures, Tables, and Boxes} Figures 4.1 4.2 5.1 5.2 14.1 The Distribution of World Income The Dimensions of Economic Lives Rich States in Europe Rich Stares in Asia U.S. Treasury Yield Curve 34 38 65 66 167 Tables Resources per Head, U.S. The World's Richest Countries Intermediate Economies What America Spends, 2001 What America Earns, 2001 Redistribution of Income Among Households, America, 2001 4.6 What America Produces, 2001 4.7 Living Standards and Productivity, 2001 4.8 Why Material Living Standards Differ, 2001 5.1 Rich and Poor States, 1820 16.1 Lighting Efficiency 16.2 Refrigerator Features 3.1 4.1 4.2 4.3 4.4 4.5 27 32 33 39 39 40 41 46 48 68 185 186 {viii} Figures, Tables and Boxes Boxes 4.1 4.2 4.3 Inequality in World Income Distribution What GDP Is, and Isn't Work and Living Standards, United States and France, 2001 12.1 Economic Rent 18.1 Happiness and Welfare 36 42 50 145 211 {Acknowledgments} • • • • • • • • • • • • • • The background research took us from the Cro-Magnon cave paintings at Lascaux to the dot.com bubble of 1999-2000, from Auckland to Zanzibar.

The link between short and long rates is called the term structure of interest rates (Figure 14.1). It is compiled by looking at rates of interest in the bond market. Banks match borrowers and lenders and allow lenders to get their money back before the borrowers repay. Bonds are another means of handling the same problem. The bond market is a secondary market, in which the right to receive repayment of a loan can be sold to someone else. Figure 14.1 U.S. Treasury Yield Curve (September 30, 2003) 0 9 10 15 Length ofbond (years) SOURCE: U.S. Treasury (Web site) 20 25 { 168} John Kay The price of a bond in this secondary market will not necessarily be the same as the original amount of the loan. The credit risk may have changed. You can today buy the debt of many telecom companies for less than half its repayment value: these companies borrowed extravagantly and many people are now skeptical of their ability to repay.


pages: 464 words: 139,088

The End of Alchemy: Money, Banking and the Future of the Global Economy by Mervyn King

"Robert Solow", Andrei Shleifer, Asian financial crisis, asset-backed security, balance sheet recession, bank run, banking crisis, banks create money, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, Bretton Woods, British Empire, business cycle, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, centre right, collapse of Lehman Brothers, creative destruction, Credit Default Swap, crowdsourcing, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, distributed generation, Doha Development Round, Edmond Halley, Fall of the Berlin Wall, falling living standards, fiat currency, financial innovation, financial intermediation, floating exchange rates, forward guidance, Fractional reserve banking, Francis Fukuyama: the end of history, full employment, German hyperinflation, Hyman Minsky, inflation targeting, invisible hand, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, labour market flexibility, large denomination, lateral thinking, liquidity trap, Long Term Capital Management, manufacturing employment, market clearing, Martin Wolf, Mexican peso crisis / tequila crisis, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, Nick Leeson, North Sea oil, Northern Rock, oil shale / tar sands, oil shock, open economy, paradox of thrift, Paul Samuelson, Ponzi scheme, price mechanism, price stability, purchasing power parity, quantitative easing, rent-seeking, reserve currency, Richard Thaler, rising living standards, Robert Shiller, Robert Shiller, Satoshi Nakamoto, savings glut, secular stagnation, seigniorage, stem cell, Steve Jobs, The Great Moderation, the payments system, The Rise and Fall of American Growth, Thomas Malthus, too big to fail, transaction costs, Tyler Cowen: Great Stagnation, yield curve, Yom Kippur War, zero-sum game

But over longer horizons still, such as a decade or more, interest rates are determined by the balance between spending and saving in the world as a whole, and central banks react to these developments when setting short-term official interest rates. Governments borrow by selling securities or bonds to the market with different periods of maturity, ranging from one month to thirty years or sometimes more. The interest rate at different maturities for such borrowing is known as the yield curve. Another important distinction is between ‘money’ and ‘real’ interest rates. Money interest rates are the usual quoted rate – if you lend $100 and after one year receive $105, the money interest rate is 5 per cent. If over the course of that year the price of the things that you like to buy is expected to rise by 5 per cent, then the ‘real’ rate of interest you earn is the money rate less the anticipated rate of inflation (in this example the real rate is zero).

Long-term bond rates, by contrast, as measured by yields on ten-year bonds, are still above zero. In late 2015, bond yields were around 2 per cent in the United States and most other advanced economies, apart from Germany and Japan, where rates were around 1 per cent and 0.5 per cent respectively. Only in Switzerland, of the major economies, were ten-year bond yields slightly negative. When the yield curve is completely flat, central banks may still create money by purchasing assets other than government bonds – either private sector assets, such as corporate bonds, or overseas currencies (the latter was the main strategy pursued by the Swiss National Bank in a vain attempt to prevent a sharp appreciation of the Swiss franc against the euro). But this means taking on credit risk of a very different kind from that involved in monetary policy, which is limited to buying and selling government bonds of different maturities, and has long been accepted as a legitimate role for central banks.


Principles of Corporate Finance by Richard A. Brealey, Stewart C. Myers, Franklin Allen

3Com Palm IPO, accounting loophole / creative accounting, Airbus A320, Asian financial crisis, asset allocation, asset-backed security, banking crisis, Bernie Madoff, big-box store, Black-Scholes formula, break the buck, Brownian motion, business cycle, buy and hold, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, carried interest, collateralized debt obligation, compound rate of return, computerized trading, conceptual framework, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cross-subsidies, discounted cash flows, disintermediation, diversified portfolio, equity premium, eurozone crisis, financial innovation, financial intermediation, fixed income, frictionless, fudge factor, German hyperinflation, implied volatility, index fund, information asymmetry, intangible asset, interest rate swap, inventory management, Iridium satellite, Kenneth Rogoff, law of one price, linear programming, Livingstone, I presume, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, market bubble, market friction, money market fund, moral hazard, Myron Scholes, new economy, Nick Leeson, Northern Rock, offshore financial centre, Ponzi scheme, prediction markets, price discrimination, principal–agent problem, profit maximization, purchasing power parity, QR code, quantitative trading / quantitative finance, random walk, Real Time Gross Settlement, risk tolerance, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, Silicon Valley, Skype, Steve Jobs, The Nature of the Firm, the payments system, the rule of 72, time value of money, too big to fail, transaction costs, University of East Anglia, urban renewal, VA Linux, value at risk, Vanguard fund, yield curve, zero-coupon bond, zero-sum game, Zipcar

With rights (cum rights, rights on) Purchase of shares in which the buyer is entitled to the rights to buy shares in the company’s rights issue (cf. ex rights). Working capital Current assets and current liabilities. The term is commonly used as synonymous with net working capital. Workout Informal arrangement between a borrower and creditors. Writer Option seller. X xd Ex dividend. xr Ex rights. Y Yankee bond A dollar bond issued in the United States by a non-U.S. borrower (cf. bulldog bond, Samurai bond). Yield curve Term structure of interest rates. Yield curve note Reverse FRN. Yield to call Yield on a bond assuming that it will be called. Yield to maturity Internal rate of return on a bond. Z Zero-coupon bond Discount bond making no coupon payments. Z-score Measure of the likelihood of bankruptcy. Index Note: Page numbers followed by n indicate material in source notes and footnotes. A Abandonment option, 259–261, 568–570 project life and, 569 temporary, 569–570 valuation of, 569 Abbott Laboratories, 842 Abnormal return, 325n, 325–326 ABS (asset-backed securities), 605, 610–611, 623, 628 Absolute priority, 459 Accelerated depreciation, 140–142 Accenture, 134n Accounting income, tax income versus, 142 Accounting rates of return, 726–728 nature of, 726–728 problems with, 728–729 Accounting standards, 643, 643n, 721, 821 Accounts payable, 756 Accounts payable period, 776–777 Accounts receivable, 755–756, 775 Accounts receivable period, 776 Accrued interest, 608 ACH (Automated Clearing House), 789 Acharya, V.

The yields increase with maturity because the term structure of spot rates is upward-sloping. Yields to maturity are complex averages of spot rates. For example, you can see that the yield on the four-year bond (5.81%) lies between the one- and four-year spot rates (3% and 6%). Financial managers who want a quick, summary measure of interest rates bypass spot interest rates and look in the financial press at yields to maturity. They may refer to the yield curve, which plots yields to maturity, instead of referring to the term structure, which plots spot rates. They may use the yield to maturity on one bond to value another bond with roughly the same coupon and maturity. They may speak with a broad brush and say, “Ampersand Bank will charge us 6% on a three-year loan,” referring to a 6% yield to maturity. Throughout this book, we too use the yield to maturity to summarize the return required by bond investors.

Return on assets (ROA) After-tax operating income as a percentage of total assets. Return on capital (ROC) After-tax operating income as a percentage of long-term capital. Return on equity (ROE) Usually, equity earnings as a proportion of the book value of equity. Return on investment (ROI) Generally, book income as a proportion of net book value. Revenue bond Municipal bond that is serviced out of the revenues from a particular project. Reverse FRN (yield curve note) Floating-rate note whose payments rise as the general level of interest rates falls and vice versa. Reverse split Action by the company to reduce the number of outstanding shares by replacing two or more of its shares with a single, more valuable share. Revolving credit Legally assured line of credit with a bank. Rights issue (privileged subscription issue) Issue of securities offered to current stockholders (cf. general cash offer).


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Unelected Power: The Quest for Legitimacy in Central Banking and the Regulatory State by Paul Tucker

Andrei Shleifer, bank run, banking crisis, barriers to entry, Basel III, battle of ideas, Ben Bernanke: helicopter money, Berlin Wall, Bretton Woods, business cycle, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, centre right, conceptual framework, corporate governance, diversified portfolio, Fall of the Berlin Wall, financial innovation, financial intermediation, financial repression, first-past-the-post, floating exchange rates, forensic accounting, forward guidance, Fractional reserve banking, Francis Fukuyama: the end of history, full employment, George Akerlof, incomplete markets, inflation targeting, information asymmetry, invisible hand, iterative process, Jean Tirole, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, liberal capitalism, light touch regulation, Long Term Capital Management, means of production, money market fund, Mont Pelerin Society, moral hazard, Northern Rock, Pareto efficiency, Paul Samuelson, price mechanism, price stability, principal–agent problem, profit maximization, quantitative easing, regulatory arbitrage, reserve currency, risk tolerance, risk-adjusted returns, road to serfdom, Robert Bork, Ronald Coase, seigniorage, short selling, Social Responsibility of Business Is to Increase Its Profits, stochastic process, The Chicago School, The Great Moderation, The Market for Lemons, the payments system, too big to fail, transaction costs, Vilfredo Pareto, Washington Consensus, yield curve, zero-coupon bond, zero-sum game

If one word, again stemming from the revolution in ideas, sums this up, it is transparency. The guiding concepts were expectations and incentives. If inflation in the short term is powerfully influenced by expectations of inflation over the medium term, then households and firms need information to help them form those expectations. If the traction of policy comes through expectations of the path of the central bank’s month-by-month decisions, as reflected in the bond-market yield curve, then the markets need information on the central bank’s approach to policy (in the jargon, its reaction function). If, then, the central bank is to be incentivized to stick to its mandate, its target and actions and the results all have to be visible. That way, it is hoist on its own reputation for competence and reliability. The complex processes through which that reputation is produced depend, crucially, on the transparency and comprehensibility of a central bank’s outputs and outcomes, on the extent to which its mandate provides for exceptional circumstances, and on the multiplicity of channels for scrutinizing it and for publicly debating its conduct and performance.14 Do not think only of the “conservative” central banker but of the “scrutinized” central banker.

I have no sense that Ben Friedman opposes CBI. 10 Similar points, without the constitutionalist framing, are made in Bernanke, “Monetary Policy.” 11 For similar sentiments, see Granville, Remembering Inflation. 12 Stein and Hanson, “Monetary Policy.” First published as a Federal Reserve research paper in 2012, this revealed that persistently easy conventional monetary policy can lead to a reduction in term premia, the compensation investors demand for taking longer-term exposures. The result was replicated for the sterling yield curve, as reported in Tucker, “National Balance Sheets.” Although not proven, this phenomenon might be driven by a search for yield by asset managers and intermediaries that are subject to nominal yield targets and/or relative performance objectives. 13 Respectively, Turner, Between Debt, and King, End of Alchemy. 14 Tucker, Financial Stability Regimes. 15 Rajan, “Step in the Dark,” p. 12. Andrew Crockett was head of the Bank for International Settlements from 1994 to 2003.

Cambridge, MA: Harvard University Press, 2001. Janowitz, Morris. “Military Elites and the Study of War.” Conflict Resolution 1, no. 1 (1957): 9–18. ________. The Professional Soldier: A Social and Political Portrait. Glencoe, IL: Free Press, 1960. Joyce, Michael A. S., Peter Lilholdt, and Steffan Sorensen. “Extracting Inflation Expectations and Inflation Risk Premia from the Term Structure: A Joint Model of the UK Nominal and Real Yield Curves.” Journal of Banking & Finance 34, no. 2 (2010): 281–94. Judge, Igor. The Safest Shield: Lectures, Speeches and Essays. Oxford: Hart Publishing, 2015. ________. “Ceding Power to the Executive: The Resurrection of Henry VIII.” Paper delivered at King’s College London, April 12, 2016. Judiciary of England and Wales. “The Accountability of the Judiciary.” October 2007. ________. Guide to Judicial Conduct.


pages: 209 words: 53,236

The Scandal of Money by George Gilder

Affordable Care Act / Obamacare, bank run, Bernie Sanders, bitcoin, blockchain, borderless world, Bretton Woods, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, Claude Shannon: information theory, Clayton Christensen, cloud computing, corporate governance, cryptocurrency, currency manipulation / currency intervention, Daniel Kahneman / Amos Tversky, Deng Xiaoping, disintermediation, Donald Trump, fiat currency, financial innovation, Fractional reserve banking, full employment, George Gilder, glass ceiling, Home mortgage interest deduction, index fund, indoor plumbing, industrial robot, inflation targeting, informal economy, Innovator's Dilemma, Internet of things, invisible hand, Isaac Newton, Jeff Bezos, John von Neumann, Joseph Schumpeter, Kenneth Rogoff, knowledge economy, Law of Accelerating Returns, Marc Andreessen, Mark Zuckerberg, Menlo Park, Metcalfe’s law, money: store of value / unit of account / medium of exchange, mortgage tax deduction, obamacare, Paul Samuelson, Peter Thiel, Ponzi scheme, price stability, Productivity paradox, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, Ray Kurzweil, reserve currency, road to serfdom, Robert Gordon, Robert Metcalfe, Ronald Reagan, Sand Hill Road, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, secular stagnation, seigniorage, Silicon Valley, smart grid, South China Sea, special drawing rights, The Great Moderation, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Tim Cook: Apple, time value of money, too big to fail, transaction costs, trickle-down economics, Turing machine, winner-take-all economy, yield curve, zero-sum game

But these quantitative changes also lavishly benefit any early borrowers or lenders of the government money who can act before related price changes propagate through the economy. Central banks currently change the money supply through a Rube Goldberg contrivance of open-market operations buying and selling Treasury notes, “quantitative easing” through purchase of private bonds and other assets, adaptive “twists” of yield curves and maturities, reserve requirements regulating bank leverage, and interest-rate manipulations that change the cost of money. These measures deny most of the users of the money any pro rata increase in their quantities during inflations and inflict borrowers with the full brunt of contractionary policy (they have to pay back their loans with more valuable units than they received). In the late 1990s, an unexpected 26 percent deflation (increase in the dollar’s value) bankrupted a thousand companies that had incurred large debts in the multifaceted process of building out the Internet with advanced fiber optics.7 By contrast, Hayek money would automatically expand or shrink the money supply in an entirely equitable and proportionate way, distributing these changes across the entire range of coin holders, with no preference for cronies, or affiliated banks, or other special interests.


Not Working by Blanchflower, David G.

active measures, affirmative action, Affordable Care Act / Obamacare, Albert Einstein, bank run, banking crisis, basic income, Berlin Wall, Bernie Madoff, Bernie Sanders, Black Swan, Boris Johnson, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, Clapham omnibus, collective bargaining, correlation does not imply causation, credit crunch, declining real wages, deindustrialization, Donald Trump, estate planning, Fall of the Berlin Wall, full employment, George Akerlof, gig economy, Gini coefficient, Growth in a Time of Debt, illegal immigration, income inequality, indoor plumbing, inflation targeting, job satisfaction, John Bercow, Kenneth Rogoff, labor-force participation, liquidationism / Banker’s doctrine / the Treasury view, longitudinal study, low skilled workers, manufacturing employment, Mark Zuckerberg, market clearing, Martin Wolf, mass incarceration, meta analysis, meta-analysis, moral hazard, Nate Silver, negative equity, new economy, Northern Rock, obamacare, oil shock, open borders, Own Your Own Home, p-value, Panamax, pension reform, plutocrats, Plutocrats, post-materialism, price stability, prisoner's dilemma, quantitative easing, rent control, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Coase, selection bias, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, South Sea Bubble, Thorstein Veblen, trade liberalization, universal basic income, University of East Anglia, urban planning, working poor, working-age population, yield curve

Officially, they are employed, but they could work more, of course. So, the amount of slack could be larger than we think. If that is the case, it’s no surprise that inflation has not kicked in.39 Sabine Lautenschläger at least has got it. There are one or two EWA indicators that were flashing amber in the United States in mid-2018. The U.S. yield curve plots Treasuries with maturities ranging from four weeks to thirty years. The gap between long and short yields turning negative has been a reliable indicator of recession. The yield curve was flattening during the first few months of 2018. Ed Yardeni’s Boom-Bust Barometer, which measures spot prices of industrial inputs like copper, steel, and lead scrap divided by initial unemployment claims, fell before or during the last two recessions. Housing starts and building permits have fallen ahead of some recent recessions.


pages: 1,066 words: 273,703

Crashed: How a Decade of Financial Crises Changed the World by Adam Tooze

Affordable Care Act / Obamacare, Apple's 1984 Super Bowl advert, Asian financial crisis, asset-backed security, bank run, banking crisis, Basel III, Berlin Wall, Bernie Sanders, Big bang: deregulation of the City of London, Boris Johnson, break the buck, Bretton Woods, BRICs, British Empire, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, Celtic Tiger, central bank independence, centre right, collateralized debt obligation, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, dark matter, deindustrialization, desegregation, Detroit bankruptcy, Dissolution of the Soviet Union, diversification, Doha Development Round, Donald Trump, Edward Glaeser, Edward Snowden, en.wikipedia.org, energy security, eurozone crisis, Fall of the Berlin Wall, family office, financial intermediation, fixed income, Flash crash, forward guidance, friendly fire, full employment, global reserve currency, global supply chain, global value chain, Goldman Sachs: Vampire Squid, Growth in a Time of Debt, housing crisis, Hyman Minsky, illegal immigration, immigration reform, income inequality, interest rate derivative, interest rate swap, Kenneth Rogoff, large denomination, light touch regulation, Long Term Capital Management, margin call, Martin Wolf, McMansion, Mexican peso crisis / tequila crisis, mittelstand, money market fund, moral hazard, mortgage debt, mutually assured destruction, negative equity, new economy, Northern Rock, obamacare, Occupy movement, offshore financial centre, oil shale / tar sands, old-boy network, open economy, paradox of thrift, Peter Thiel, Ponzi scheme, predatory finance, price stability, private sector deleveraging, purchasing power parity, quantitative easing, race to the bottom, reserve currency, risk tolerance, Ronald Reagan, savings glut, secular stagnation, Silicon Valley, South China Sea, sovereign wealth fund, special drawing rights, structural adjustment programs, The Great Moderation, Tim Cook: Apple, too big to fail, trade liberalization, upwardly mobile, Washington Consensus, We are the 99%, white flight, WikiLeaks, women in the workforce, Works Progress Administration, yield curve, éminence grise

It was fine-tuning, not shock and awe. The mortgage boom continued undeterred, as did global demand for American safe assets and the expansion of the shadow banking sector. By the spring of 2006, to the alarm of many commentators, the result was that the yield curve was inverted. Long-term rates were below the short-term interest rates set by the Fed. This was usually a signal for trouble. It meant that the normal bank-funding model of borrowing short to lend long no longer made any sense. In due course, the inversion of the yield curve might by itself have produced a recession. But it wasn’t Greenspan or Bernanke who killed the mortgage boom. It killed itself. By 2005 at the latest it was clear that low-quality mortgage debt was a ticking bomb. Many of the subprime mortgages were on balloon rates that would rapidly increase after a period of two or three years.

See International Monetary Fund (IMF) India, 477, 483 Indonesia, 477, 483 Asian financial crisis and, 7, 32, 255–56, 261 capital controls adopted by, 475 financial crisis of 2008 and, 258–59 stimulus program, 258–59 Industrial and Commercial Bank of China, 249 inequality, 455–63 inflation, 11, 44–45 ING, 124 interest rates ECB rate increase 2011, 378–79 Fed’s rate hikes, 2015–2018, 590 Greenspan cuts, after dot-com bust and 9/11, 37–38, 55–56, 69–70 inverted yield curve, 2006, 70 taper tantrum of 2013, 472–82 Volcker’s raising of, 43–44 intergovernmentalism, 113, 114–15 International Monetary Fund (IMF), 17–18, 89, 127 acceptance of exchange controls, 475 Asian financial crisis, 261 banking crisis, 206, 401-2 Eastern European crisis, 127, 230–32, 235, 237, 491–93 eurozone crisis, 2010–12, Greece and, 323, 325, 332–34, 336, 340, 343, 344–45, 357, 377, 382–85, 388–89, 405, 413, 422, 424 eurozone crisis, 2015, Greece and 516–517, 520, 523, 527, 528–30 eurozone crisis Ireland and, 360, 364–65, 368, 398 eurozone crisis Italy and, 410–11 fiscal multiplier underestimated by, 423, 429–30 global imbalances, 40, 370 G20 agreement for expanded funding of, 270, 272 quota reallocation, 270, 272, 469, 479, 488 Ukraine and 2013–15, 493–94, 495–98, 500–1, 507–8 warns against Brexit, 550 investment banks, 51–54 Brexit and, 550–51 compensation at, 65 crises faced by, in 1990s and early 2000s, 53–54 funding of, 52–53 growth from 1970s, 51–53 products engineered by, 52 profits made by, 64–65 See also specific investment banks Iraq War, 3, 28, 115–16 Ireland, 83–84, 167, 337, 338 bank bailouts in, 185–86, 193 debt crisis in, 322, 323, 359–66 ECB forces austerity plan on, 362–65 household wealth lost in, 156 IMF and, 360, 364–65, 368, 398 real estate boom in, 105, 106, 107, 109 Irish Times, 363 Irwin, Neil, 215–17, 350 Italy, 322, 385 austerity program adopted in, 387 Cannes G20 and, 410–12 debt of, 386–87, 389 ECB’s bond buying program, 2011, 398–99 euro entry of, 94 eurozone crisis resolution, 2012 and, 431–37 Japan, 30, 158–59 Jay Z, 40 Johnson, Boris, 548, 552 J.P.


pages: 274 words: 60,596

Millionaire Teacher: The Nine Rules of Wealth You Should Have Learned in School by Andrew Hallam

Albert Einstein, asset allocation, Bernie Madoff, buy and hold, diversified portfolio, financial independence, George Gilder, index fund, Long Term Capital Management, new economy, passive investing, Paul Samuelson, Ponzi scheme, pre–internet, price stability, random walk, risk tolerance, Silicon Valley, South China Sea, stocks for the long run, survivorship bias, transaction costs, Vanguard fund, yield curve

Part of her job is to compose confusing market-summary commentaries that often accompany mutual fund statements. They read something like this: Stocks fell this month because retail sales were off 2.5 percent, creating a surplus of gold buyers over denim, which will likely raise Chinese futures on the backs of the growing federal deficit, which caused two Wall Street Bankers to streak through Central Park because of the narrowing bond yield curve. Saying stock markets rose this year because more polar bears were able to find suitable mates before November has as much merit as the confusing economic drivel that financial planners write and distribute, assuming that nobody will read it anyway. If you ask her, she will tell you that actively managed mutual funds are the way to go—but curiously doesn’t mention she has killer mortgage payments on her $17 million, Hawaiian beachside summer home and you need to help her pay it.


pages: 240 words: 60,660

Models. Behaving. Badly.: Why Confusing Illusion With Reality Can Lead to Disaster, on Wall Street and in Life by Emanuel Derman

Albert Einstein, Asian financial crisis, Augustin-Louis Cauchy, Black-Scholes formula, British Empire, Brownian motion, capital asset pricing model, Cepheid variable, creative destruction, crony capitalism, diversified portfolio, Douglas Hofstadter, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, Henri Poincaré, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, Isaac Newton, Johannes Kepler, law of one price, Mikhail Gorbachev, Myron Scholes, quantitative trading / quantitative finance, random walk, Richard Feynman, riskless arbitrage, savings glut, Schrödinger's Cat, Sharpe ratio, stochastic volatility, the scientific method, washing machines reduced drudgery, yield curve

There are sweetness and tartness, spiciness and blandness, smoothness and lumpiness, all of them different types of gustatory pleasure. And I have ignored other kinds of mentionable and unmentionable bodily pleasures, which have their pleasure premiums too. Similarly, there is more than one kind of risk and more than one kind of risk premium: stock risk and bond risk and currency risk and commodity risk and slope-of-the-yield-curve risk; and within the universe of stocks there is sector risk—health risk, technology risk, consumer durables risk, et cetera. In physics the values of the fundamental constants (the gravitational constant G, the electric charge e, Planck’s constant h, the speed of light c) are apparently timeless and universal. I doubt there will ever be a universal value for the risk premium. THE EMM AND THE BLACK-SCHOLES MODEL The best model in all of economics is the Black-Scholes Model for valuing options on stocks, an ingeniously clever extension of the EMM published in 1973 by Fischer Black and Myron Scholes.a I spent my first two years at Goldman Sachs, 1986–1987, working with Fischer Black on an extension of this model to valuing options on bonds,12 and I devoted 1993–1994 to working on an extension of Black-Scholes to stocks with variable volatility.


pages: 597 words: 172,130

The Alchemists: Three Central Bankers and a World on Fire by Neil Irwin

"Robert Solow", Ayatollah Khomeini, bank run, banking crisis, Berlin Wall, Bernie Sanders, break the buck, Bretton Woods, business climate, business cycle, capital controls, central bank independence, centre right, collapse of Lehman Brothers, collateralized debt obligation, credit crunch, currency peg, eurozone crisis, financial innovation, Flash crash, George Akerlof, German hyperinflation, Google Earth, hiring and firing, inflation targeting, Isaac Newton, Julian Assange, low cost airline, market bubble, market design, money market fund, moral hazard, mortgage debt, new economy, Northern Rock, Paul Samuelson, price stability, quantitative easing, rent control, reserve currency, Robert Shiller, Robert Shiller, rolodex, Ronald Reagan, savings glut, Socratic dialogue, sovereign wealth fund, The Great Moderation, too big to fail, union organizing, WikiLeaks, yield curve, Yom Kippur War

The success of the approach suggested an interesting possibility: If the Fed were to shift its portfolio into longer-term bonds and away from shorter-term debt, it might be able to lower long-term interest rates across the economy, encouraging business investment and mortgage borrowing. The action would have the effect of simultaneously raising short-term interest rates, but only negligibly, because the Fed had already committed to keeping them near zero. Theoretically, borrowing would be inexpensive in both the short and the long term. It’s called twisting the yield curve, and a variation had been tried at the central bank in 1961, when it was called Operation Twist, a not terribly sly reference to the dance craze of the time. The Bernanke Fed, with its culture of seriousness, called the strategy a Maturity Extension Program. But to the media, Operation Twist, with its attendant possibility of Chubby Checker–based headline puns, was an irresistible way to describe the policy.

See also European Central Bank (ECB) remedies background information, 12, 112–15 beginning crisis, view of, 7, 128, 135, 137 BNP Paribas crisis as first, 1–3 on coordination of remedies, 159–61 economic orientation of, 115, 287 Eurogroup meeting protest, 306–7 Franco-German Declaration criticism by, 290–92 -Geithner relationship, 219, 317 Governing Council meetings, role at, 136–37 on Greek financial crisis, 204, 206–8, 211–12, 218–19, 222–23, 287 on Italy/Spain crises, 317–23 at Jackson Hole conference, 97 on Lehman failure, 143 at Maastricht negotiations, 77, 114 nomination as ECB president, 81–82 personal traits, 114–15 poor decisions of, 135–37, 212–13, 303–5 as “president” of Europe, 322 retirement gala, 324–26 -Sarkozy dispute, 324 successor to. See Draghi, Mario on Term Auction Facility (TAF), 131–32 True Finns, 297 Trust Company of America, 41 Tucker, Paul, 241, 388 Twisting the yield curve, 331–32 Tyrie, Andrew, 250 Ueda, Kazuo, 89, 91–92 Ullstein, Leopold, 52 Unemployment Great Britain (2009–2011), 236, 248, 251, 334 Great Depression era, 57, 58, 60 during Greenspan tenure, 94, 99 during inflation of 1970s, 65 Ireland (2010), 284 level in 2009, 188 U.S. weak jobs growth, 259, 268–69, 328, 378 United Copper, 40–41 United States annual growth needs, 267 central bank.


pages: 632 words: 166,729

Addiction by Design: Machine Gambling in Las Vegas by Natasha Dow Schüll

airport security, Albert Einstein, Build a better mousetrap, business intelligence, capital controls, cashless society, commoditize, corporate social responsibility, deindustrialization, dematerialisation, deskilling, game design, impulse control, information asymmetry, inventory management, iterative process, jitney, large denomination, late capitalism, late fees, longitudinal study, means of production, meta analysis, meta-analysis, Nash equilibrium, Panopticon Jeremy Bentham, post-industrial society, postindustrial economy, profit motive, RFID, Silicon Valley, Slavoj Žižek, statistical model, the built environment, yield curve, zero-sum game

Mariposa’s “player contact” component has incorporated a similar feature that color codes player’s floor icons to indicate whether they are expected to increase in value (green), decline in value (red), or remain the same (yellow). 48. Wilson 2007. Despite the argument for the emotion-removing capacities of visual analytic tools, it should be noted that such tools can also be a source of anxiety and other forms of affect on the part of analysts, as the anthropologist Zaloom (2009) has pointed out in the case of the “yield curve.” For more work on how financial professionals have used visual tools to model the market and thus better intervene in it, see Knorr Cetina and Breugger 2002; Mackenzie 2006; Preda 2006; Zaloom 2006. Tools for visualizing the market are part of a more general contemporary trend in which “narrative, models, and scenarios [are devised to] capture in useful ways the uncertainties, contingencies, and calculations of risk that complex technologies and interactions inherently generate” (Fischer 2003, 2). 49.

“Leaving Las Vegas: Does Exposure to Las Vegas Increase Risk for Suicide?” Social Science and Medicine 67: 1882–88. Young, Martin, M. Stevens, and W. Tyler. 2006. Northern Territory Gambling Prevalence Survey 2005. School for Social and Policy Research, Charles Darwin University. Zaloom, Caitlin. 2006. Out of the Pits: Traders and Technology from Chicago to London. Chicago: University of Chicago Press. ———. 2009. “How to Read the Future: The Yield Curve, Affect, and Financial Prediction.” Public Culture 21: 2. ———. 2010. “The Derivative World.” The Hedgehog Review (Summer). Zangeneh, Masood, and T. Hason. 2006. “Suicide and Gambling.” International Journal of Mental Health and Addiction 4 (3): 191–93. Zangeneh, Masood, and E. Haydon. 2004. “Psycho-Structural Cybernetic Model, Feedback and Problem Gambling: A New Theoretical Approach.” International Journal of Mental Health and Addiction 1 (2): 25–31.


pages: 224 words: 13,238

Electronic and Algorithmic Trading Technology: The Complete Guide by Kendall Kim

algorithmic trading, automated trading system, backtesting, commoditize, computerized trading, corporate governance, Credit Default Swap, diversification, en.wikipedia.org, family office, financial innovation, fixed income, index arbitrage, index fund, interest rate swap, linked data, market fragmentation, money market fund, natural language processing, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, short selling, statistical arbitrage, Steven Levy, transaction costs, yield curve

However, despite regulatory intervention, corporate bond trades are reported within a 15-minute time span and not real time.6 In July 2006, the CBOT introduced a pilot program for algorithms utilized for two- and five-year Treasury futures. The pilot program was implemented to assess the impact on trading profiles and behavior; to identify the demographics of participants pre- and post-pilot implementation; to determine whether the change in algorithm impacts the number of participants in a contract; and to assess the growth rate of the five-year Treasury Note contracts benchmarked against relevant instruments along the yield curve. The program was designed to monitor a straight First In First Out (FIFO) algorithm, which matches trades on a strict time and price priority, versus a pro rata algorithm, which matches trades based on a distributed proportionate approach. The exchange will continue to change in contract volume, participation levels, and order management behavior.7 6.8 Conclusion Algorithms are designed to balance a juggling act.


pages: 206 words: 70,924

The Rise of the Quants: Marschak, Sharpe, Black, Scholes and Merton by Colin Read

"Robert Solow", Albert Einstein, Bayesian statistics, Black-Scholes formula, Bretton Woods, Brownian motion, business cycle, capital asset pricing model, collateralized debt obligation, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, David Ricardo: comparative advantage, discovery of penicillin, discrete time, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, floating exchange rates, full employment, Henri Poincaré, implied volatility, index fund, Isaac Newton, John Meriwether, John von Neumann, Joseph Schumpeter, Kenneth Arrow, Long Term Capital Management, Louis Bachelier, margin call, market clearing, martingale, means of production, moral hazard, Myron Scholes, Paul Samuelson, price stability, principal–agent problem, quantitative trading / quantitative finance, RAND corporation, random walk, risk tolerance, risk/return, Ronald Reagan, shareholder value, Sharpe ratio, short selling, stochastic process, Thales and the olive presses, Thales of Miletus, The Chicago School, the scientific method, too big to fail, transaction costs, tulip mania, Works Progress Administration, yield curve

There, he continued to work in options and, increasingly, in improving finance’s understanding of interest rates. Black saw the description and prediction of interest rates to be a multi-faceted and challenging problem. While he had demonstrated that an options price depends on the underlying stock price mean and volatility, and the risk-free interest rate, the overall market for interest rates is much more multi-dimensional. The interest rate yield curve, which graphs rates against maturities, depends on many markets and instruments, each of which is subject to stochastic processes. His interest and collaboration with Emanuel Derman and Bill Toy resulted in a model of interest rates that was first used profitably by Goldman Sachs through the 1980s, but eventually entered the public domain when they published their work in the Financial Analysts Journal in 1990.2 Their model provided reasonable estimates for both the prices and volatilities of treasury bonds, and is still used today.


pages: 213 words: 70,742

Notes From an Apocalypse: A Personal Journey to the End of the World and Back by Mark O'Connell

Berlin Wall, bitcoin, blockchain, California gold rush, carbon footprint, Carrington event, clean water, Colonization of Mars, conceptual framework, cryptocurrency, disruptive innovation, diversified portfolio, Donald Trump, Donner party, Elon Musk, high net worth, Jeff Bezos, life extension, low earth orbit, Marc Andreessen, Mikhail Gorbachev, mutually assured destruction, New Urbanism, off grid, Peter Thiel, post-work, Sam Altman, Silicon Valley, Stephen Hawking, Steven Pinker, the built environment, yield curve

The UN had announced sanctions against North Korea, and North Korea had vowed to take physical action against such sanctions, and America, in the person of a president who was at that point vacationing at one of his many eponymous luxury golf resorts, advised that if they so much as lifted a finger they would be met with “fire and fury like the world has never seen.” According to The Wall Street Journal, analysts were trying to guess what would happen to the markets in the event of all-out nuclear war between the United States and North Korea. (The answer seemed to be that you would likely see some flattening of yield curves due to lower risk appetites, but that from a financial perspective a nuclear apocalypse wouldn’t exactly be the end of the world.) The apocalypse was trending. The memes were dank with foreboding, and the presiding mood was one of half-ironic Cold War nostalgia mixed with sincere eschatological unease. It seemed as good a time as any to visit a place for sitting out the end times. My obsessive consumption of prepper videos had opened out onto a broader vista of apocalyptic preparedness, and to a lucrative niche of the real estate sector catering to individuals of means who wanted a place to retreat to when the shit truly hit the fan.


pages: 242 words: 71,943

Strong Towns: A Bottom-Up Revolution to Rebuild American Prosperity by Charles L. Marohn, Jr.

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, A Pattern Language, American Society of Civil Engineers: Report Card, bank run, big-box store, Black Swan, Bretton Woods, British Empire, business cycle, call centre, cognitive dissonance, complexity theory, corporate governance, Detroit bankruptcy, Donald Trump, en.wikipedia.org, facts on the ground, Ferguson, Missouri, global reserve currency, housing crisis, index fund, Jane Jacobs, Jeff Bezos, low skilled workers, mass immigration, mortgage debt, Network effects, new economy, New Urbanism, paradox of thrift, Paul Samuelson, pensions crisis, Ponzi scheme, quantitative easing, reserve currency, the built environment, The Death and Life of Great American Cities, trickle-down economics, Upton Sinclair, urban planning, urban renewal, walkable city, white flight, women in the workforce, yield curve, zero-sum game

How does a measurement like inflation have any credibility – or real meaning – when health care, college tuition, home prices, and construction costs have increased annually for decades at rates much greater? What is lost in all the centralization and efficiency is local nuance, or what most people would consider real meaning. The Difference Between Growth and Wealth Going into the summer of 2005, there was general concern among economists and money managers that a recession was imminent. The yield curve was flattening as investors bought longer-term notes to lock in higher rates ahead of possible interest rate declines.9 The Dow Jones average was down 7% from the start of the year.10 The Federal Reserve was raising rates to get some wiggle room should the anticipated rate-cutting stimulus be needed.11 Indicators were moving in the wrong direction, and then at the end of August came Hurricane Katrina, which destroyed large portions of New Orleans and the Mississippi Gulf Coast.


The Handbook of Personal Wealth Management by Reuvid, Jonathan.

asset allocation, banking crisis, BRICs, business cycle, buy and hold, collapse of Lehman Brothers, correlation coefficient, credit crunch, cross-subsidies, diversification, diversified portfolio, estate planning, financial deregulation, fixed income, high net worth, income per capita, index fund, interest rate swap, laissez-faire capitalism, land tenure, market bubble, merger arbitrage, negative equity, new economy, Northern Rock, pattern recognition, Ponzi scheme, prediction markets, Right to Buy, risk tolerance, risk-adjusted returns, risk/return, short selling, side project, sovereign wealth fund, statistical arbitrage, systematic trading, transaction costs, yield curve

Many funds’ computer models were re-calibrated following the restrictions on shorting financials. 2009 outlook Those hedge funds that have preserved capital through 2008 will have the financial fire power to take advantage of the opportunities now arising. As distressed sellers are unloading cheap assets to unwind their over-leverage, it presents opportunities for relative value traders, who may be able to hedge out various market risks. In the commodities sector, dislocations between cash and futures prices are increasing. Macro managers may also be able to benefit from steepening yield curves following the sharp interest rate cuts of 1.5 per cent on 6 November 2008 in the UK (with other countries following suit), the further rate cut of 1 per cent in December and the possibility of further cuts to follow. Distressed managers are beginning to see a huge number of potential opportunities emerging. Corporate default rates are at current historic lows and are set to rise throughout 2009.


pages: 183 words: 17,571

Broken Markets: A User's Guide to the Post-Finance Economy by Kevin Mellyn

banking crisis, banks create money, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, Bonfire of the Vanities, bonus culture, Bretton Woods, BRICs, British Empire, business cycle, buy and hold, call centre, Carmen Reinhart, central bank independence, centre right, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, corporate raider, creative destruction, credit crunch, crony capitalism, currency manipulation / currency intervention, disintermediation, eurozone crisis, fiat currency, financial innovation, financial repression, floating exchange rates, Fractional reserve banking, global reserve currency, global supply chain, Home mortgage interest deduction, index fund, information asymmetry, joint-stock company, Joseph Schumpeter, labor-force participation, light touch regulation, liquidity trap, London Interbank Offered Rate, market bubble, market clearing, Martin Wolf, means of production, mobile money, money market fund, moral hazard, mortgage debt, mortgage tax deduction, negative equity, Ponzi scheme, profit motive, quantitative easing, Real Time Gross Settlement, regulatory arbitrage, reserve currency, rising living standards, Ronald Coase, seigniorage, shareholder value, Silicon Valley, statistical model, Steve Jobs, The Great Moderation, the payments system, Tobin tax, too big to fail, transaction costs, underbanked, Works Progress Administration, yield curve, Yogi Berra, zero-sum game

If some debt instruments offer better yields than prime corporate and government bonds, you are probably taking on a lot more risk. The Fed policy is literally forcing institutional investors to take on more and more risk in search of returns, and certain exotic structured products such as collateralized debt obligations are creeping back into the market. With the central banks flooding the market with liquidity, the market is too distorted and the yield curves too flat (long-term and short-term rates are about the same) for ordinary investors to navigate. Also, don’t take for granted that money market funds are risk free in today’s world. Stay Debt Free Fifth, not spending money is as good as earning more money in a repressed economy. For starters, if your portfolio is doing a bit better than the market, but you are paying substantial management or advisor fees, it can cancel out.


pages: 268 words: 74,724

Who Needs the Fed?: What Taylor Swift, Uber, and Robots Tell Us About Money, Credit, and Why We Should Abolish America's Central Bank by John Tamny

Airbnb, bank run, Bernie Madoff, bitcoin, Bretton Woods, buy and hold, Carmen Reinhart, corporate raider, correlation does not imply causation, creative destruction, Credit Default Swap, crony capitalism, crowdsourcing, Donald Trump, Downton Abbey, fiat currency, financial innovation, Fractional reserve banking, full employment, George Gilder, Home mortgage interest deduction, Jeff Bezos, job automation, Joseph Schumpeter, Kenneth Rogoff, Kickstarter, liquidity trap, Mark Zuckerberg, market bubble, money market fund, moral hazard, mortgage tax deduction, NetJets, offshore financial centre, oil shock, peak oil, Peter Thiel, price stability, profit motive, quantitative easing, race to the bottom, Ronald Reagan, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Steve Jobs, The Wealth of Nations by Adam Smith, too big to fail, Travis Kalanick, Uber for X, War on Poverty, yield curve

The common answer to the above is that markets were so overly manipulated that equity prices reflected their being the only game in town for investors who wanted some semblance of return. But to believe this, one would have to believe that central bankers suddenly figured out how to engineer bull markets. The problem with such an assertion, particularly one that says low rates push investors into stocks, is that the latter has been policy from the Bank of Japan since the 1990s. Low interest rates across the yield curve have long been the norm for Japan’s central bank, as has quantitative easing (Japan’s economy has suffered 10 doses of QE from the Bank of Japan10). Yet, the Nikkei 225 is still half of what it was in the late 1980s. Moving to China, its stock markets started to buckle in August 2015. Worried about stocks falling further, the Chinese government spent tens of billions of yuan trying to prop the market up.11 It failed.


pages: 700 words: 201,953

The Social Life of Money by Nigel Dodd

accounting loophole / creative accounting, bank run, banking crisis, banks create money, Bernie Madoff, bitcoin, blockchain, borderless world, Bretton Woods, BRICs, business cycle, capital controls, cashless society, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computer age, conceptual framework, credit crunch, cross-subsidies, David Graeber, debt deflation, dematerialisation, disintermediation, eurozone crisis, fiat currency, financial exclusion, financial innovation, Financial Instability Hypothesis, financial repression, floating exchange rates, Fractional reserve banking, German hyperinflation, Goldman Sachs: Vampire Squid, Hyman Minsky, illegal immigration, informal economy, interest rate swap, Isaac Newton, John Maynard Keynes: Economic Possibilities for our Grandchildren, joint-stock company, Joseph Schumpeter, Kickstarter, Kula ring, laissez-faire capitalism, land reform, late capitalism, liberal capitalism, liquidity trap, litecoin, London Interbank Offered Rate, M-Pesa, Marshall McLuhan, means of production, mental accounting, microcredit, mobile money, money market fund, money: store of value / unit of account / medium of exchange, mortgage debt, negative equity, new economy, Nixon shock, Occupy movement, offshore financial centre, paradox of thrift, payday loans, Peace of Westphalia, peer-to-peer, peer-to-peer lending, Ponzi scheme, post scarcity, postnationalism / post nation state, predatory finance, price mechanism, price stability, quantitative easing, quantitative trading / quantitative finance, remote working, rent-seeking, reserve currency, Richard Thaler, Robert Shiller, Robert Shiller, Satoshi Nakamoto, Scientific racism, seigniorage, Skype, Slavoj Žižek, South Sea Bubble, sovereign wealth fund, special drawing rights, The Wealth of Nations by Adam Smith, too big to fail, trade liberalization, transaction costs, Veblen good, Wave and Pay, Westphalian system, WikiLeaks, Wolfgang Streeck, yield curve, zero-coupon bond

Eurozone member states were able to borrow at lower interest rates because creditors (mostly bondholders) were treating them as part of a homogeneous financial space. That is to say, the introduction of the euro appeared to coincide with the unification of government bond yields across the Eurozone. Member states were borrowing at similar rates, reflecting that their debt carried a similar underlying degree of risk. It was as if a single yield curve had been established for the bonds issued by all Eurozone member states (Aglietta and Scialom 2003: 52). The effect of those lower borrowing costs on Greece was especially striking: starting with a yield of more than 11 percent in the beginning of 1998, Greek borrowing costs declined constantly to about 6 percent in mid-2000 and even further to a low at 3.3 percent in September 2005.36 Similar examples can be seen among more recent entrants to Euroland, Slovenia and Slovakia.

The effect of those lower borrowing costs on Greece was especially striking: starting with a yield of more than 11 percent in the beginning of 1998, Greek borrowing costs declined constantly to about 6 percent in mid-2000 and even further to a low at 3.3 percent in September 2005.36 Similar examples can be seen among more recent entrants to Euroland, Slovenia and Slovakia. On joining the euro, both experienced a rapid lowering of bond rates. Indeed, almost all newly joining countries have experienced a boom upon joining the Eurozone. If the Eurozone resembled a monetary and financial union during its first few years, it turned out to be an illusion. That single yield curve for sovereign bonds splintered midway through 2008, and spreads have been widening ever since. So why have rates diverged? One simple answer is that debt has been used in a different way since the global crisis. De Grauwe, for example, points to the “flight to safety” of investors dumping private debt and turning to low-risk sovereign debt. Crucially, this statement means that those Eurozone governments with a stronger reputation have enjoyed a lowering of rates, whereas those countries considered weaker could not draw the same benefit.


pages: 321

Finding Alphas: A Quantitative Approach to Building Trading Strategies by Igor Tulchinsky

algorithmic trading, asset allocation, automated trading system, backtesting, barriers to entry, business cycle, buy and hold, capital asset pricing model, constrained optimization, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, financial intermediation, Flash crash, implied volatility, index arbitrage, index fund, intangible asset, iterative process, Long Term Capital Management, loss aversion, market design, market microstructure, merger arbitrage, natural language processing, passive investing, pattern recognition, performance metric, popular capitalism, prediction markets, price discovery process, profit motive, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, selection bias, sentiment analysis, shareholder value, Sharpe ratio, short selling, Silicon Valley, speech recognition, statistical arbitrage, statistical model, stochastic process, survivorship bias, systematic trading, text mining, transaction costs, Vanguard fund, yield curve

Traditionally, the correlation between price and volatility is negative for equities, which generally are a risk-on asset. Therefore, high or increasing VIX levels are associated with money moving out of equity markets into safer assets, indicating the arrival of a risk-off regime. The VIX itself is a tradable futures instrument that is used by many to benefit from falling markets. Other indicators include the yield curve (higher and steeper is risk-on; lower and flatter or inverted is risk-off); sector flows among risk-on sectors such as consumer discretionary and risk-­ off sectors such as utilities, or among emerging (risk-on) and developed (risk-off) markets; carry currency pairs, such as AUD/JPY; and the covariance structure of the market (the top eigenvector is usually risk-off). Risk-on/risk-off alphas can be traded on the VIX and on broad cross-­ sectional markets because they are based on broad market correlations.


pages: 268 words: 81,811

Flash Crash: A Trading Savant, a Global Manhunt, and the Most Mysterious Market Crash in History by Liam Vaughan

algorithmic trading, backtesting, bank run, barriers to entry, Bernie Madoff, Black Swan, Bob Geldof, centre right, collapse of Lehman Brothers, Donald Trump, Elliott wave, eurozone crisis, family office, Flash crash, high net worth, High speed trading, information asymmetry, Jeff Bezos, Kickstarter, margin call, market design, market microstructure, Nick Leeson, offshore financial centre, pattern recognition, Ponzi scheme, Ralph Nelson Elliott, Ronald Reagan, sovereign wealth fund, spectrum auction, Stephen Hawking, the market place, Tobin tax, tulip mania, yield curve, zero-sum game

They adored him, happily sacrificing a share of their profits for the opportunity to make a fortune. Paolo was among them. Brought up with his brother in south London by their mother, a housewife, and father, a police detective, Paolo found school easy and took his math exam early, but he was restless, and when his friends went off to university, he landed a junior role at the Bank of England. After a couple of fusty years behind a desk learning about interest rates and yield curves, he got a position at a merchant bank, where he worked in a department that used futures to hedge its portfolios. One day a broker invited him on a tour of Liffe. They met by the Royal Exchange’s towering stone columns at 1:25 p.m., five minutes before a big economic announcement was due to be made. “Even now, thinking about it, the hairs on the back of my neck stand up,” Rossi recalls.


pages: 335 words: 94,657

The Bogleheads' Guide to Investing by Taylor Larimore, Michael Leboeuf, Mel Lindauer

asset allocation, buy and hold, buy low sell high, corporate governance, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, Donald Trump, endowment effect, estate planning, financial independence, financial innovation, high net worth, index fund, late fees, Long Term Capital Management, loss aversion, Louis Bachelier, margin call, market bubble, mental accounting, money market fund, passive investing, Paul Samuelson, random walk, risk tolerance, risk/return, Sharpe ratio, statistical model, stocks for the long run, survivorship bias, the rule of 72, transaction costs, Vanguard fund, yield curve, zero-sum game

Turnover rate: An indication of the manager's trading activity during the past year. Unrealized capital gain/loss: A gain or loss that would be realized if the fund's securities were sold. Wash sale: An IRS rule that disallows the loss from the sale of a fund if the investor invests in a "substantially identical" fund within 31 days. Yield: Income received from an investment expressed as a percentage of its current price. Yield curve: A line on a graph that depicts the yields of bonds of varying maturities. APPENDIX II Books We Recommend BOOKS FOR NOVICE INVESTORS The Coffeehouse Investor by Bill Shultheis (Kirkland, WA: Palouse Press, 2005). A little book with a big message: How to invest simply and successfully. The Millionaire in You by Michael LeBoeuf (New York: Crown Business, 2002). A primer on how to invest money and time intelligently to achieve financial freedom.


pages: 345 words: 87,745

The Power of Passive Investing: More Wealth With Less Work by Richard A. Ferri

asset allocation, backtesting, Bernie Madoff, buy and hold, capital asset pricing model, cognitive dissonance, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, endowment effect, estate planning, Eugene Fama: efficient market hypothesis, fixed income, implied volatility, index fund, intangible asset, Long Term Capital Management, money market fund, passive investing, Paul Samuelson, Ponzi scheme, prediction markets, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, survivorship bias, too big to fail, transaction costs, Vanguard fund, yield curve, zero-sum game

unrealized capital gain/loss An increase (or decrease) in the value of a security that is not yet realized because the security has not been sold. volatility The degree of fluctuation in the value of a security, mutual fund, or index. Volatility is often expressed as a mathematical measure, such as a standard deviation or beta. The greater a fund’s volatility, the wider the fluctuations between highs and lows. yield curve A line plotted on a graph that depicts the yields of bonds of varying maturities, from short-term to long-term. The line, or curve, shows the relationship between short- and long-term interest rates. yield-to-maturity The rate of return an investor would receive if the securities held in his or her portfolio were held until their maturity dates. Notes Preface 1. John C. Bogle, “The Chief Cornerstone,” (speech, Superbowl of Indexing conference, Phoenix, Arizona, December 7, 2005). 2.


pages: 314 words: 101,452

Liar's Poker by Michael Lewis

barriers to entry, Bonfire of the Vanities, business cycle, cognitive dissonance, corporate governance, corporate raider, financial independence, financial innovation, fixed income, Home mortgage interest deduction, interest rate swap, Irwin Jacobs, John Meriwether, London Interbank Offered Rate, margin call, mortgage tax deduction, nuclear winter, Ponzi scheme, The Predators' Ball, yield curve

Says Ranieri: "I stopped trying to argue with customers about prepayments and finally started talking price. At what price were they attractive? There had to be some price where the customers would buy. A hundred basis points over treasuries [meaning one percentage point yield greater than U.S. treasury bonds]? Two hundred basis points? I mean, these things were three hundred and fifty basis points off the [U.S. treasury yield] curve!" All American homeowners had a feel for the value of the right to repay their mortgage at any time. They knew if they borrowed money when interest rates were high that they could pay it back once rates fell and reborrow at the lower rates. They liked having that option. Presumably they would be willing to pay for the option. But no one even on Wall Street could put a price on the homeowners' option (and people still can't, though they're getting closer).


Systematic Trading: A Unique New Method for Designing Trading and Investing Systems by Robert Carver

asset allocation, automated trading system, backtesting, barriers to entry, Black Swan, buy and hold, cognitive bias, commodity trading advisor, Credit Default Swap, diversification, diversified portfolio, easy for humans, difficult for computers, Edward Thorp, Elliott wave, fixed income, implied volatility, index fund, interest rate swap, Long Term Capital Management, margin call, merger arbitrage, Nick Leeson, paper trading, performance metric, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, survivorship bias, systematic trading, technology bubble, transaction costs, Y Combinator, yield curve

If the world doesn’t change this will be equal to the yield on the asset, less the cost of borrowing (or the interest you would have got from holding cash). So if you buy shares in French supermarket Carrefour which currently has a dividend yield of 3%, and you pay 1% to borrow the purchase cost, then you earn 2% carry on the position if the share price is unchanged. Similarly today I can buy June 2018 Eurodollar futures at 97.94, or March 2018 at 98.01. If there is no change in the shape of the yield curve then in three months’ time June futures will rise to 98.01, earning 0.07 per contract. Academic theory predicts that prices should move against us to offset these returns, but it often doesn’t. Carry is usually earned steadily on these kinds of trades although occasionally they go horribly wrong and the relationship temporarily breaks down, giving this trading rule some evil negative skew. I think the rule is profitable as a reward for taking on this nasty skew and various other kinds of risk premium which tend to be correlated to carry trades.


Falter: Has the Human Game Begun to Play Itself Out? by Bill McKibben

23andMe, Affordable Care Act / Obamacare, Airbnb, American Legislative Exchange Council, Anne Wojcicki, artificial general intelligence, Bernie Sanders, Bill Joy: nanobots, Burning Man, call centre, carbon footprint, Charles Lindbergh, clean water, Colonization of Mars, computer vision, David Attenborough, Donald Trump, double helix, Edward Snowden, Elon Musk, ending welfare as we know it, energy transition, Flynn Effect, Google Earth, Hyperloop, impulse control, income inequality, Intergovernmental Panel on Climate Change (IPCC), Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, life extension, light touch regulation, Mark Zuckerberg, mass immigration, megacity, Menlo Park, moral hazard, Naomi Klein, Nelson Mandela, obamacare, off grid, oil shale / tar sands, pattern recognition, Peter Thiel, plutocrats, Plutocrats, profit motive, Ralph Waldo Emerson, Ray Kurzweil, Robert Mercer, Ronald Reagan, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, smart meter, Snapchat, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, supervolcano, technoutopianism, The Wealth of Nations by Adam Smith, traffic fines, Travis Kalanick, urban sprawl, Watson beat the top human players on Jeopardy!, Y Combinator, Y2K, yield curve

And economics professors, it turns out, “give significantly less money to charity than their worse-paid colleagues in many other disciplines.”11 This is the world where think tanks debate whether it’s cost-effective to save the Arctic, and where the Wall Street Journal runs a headline such as HOW DO YOU PRICE A PROBLEM LIKE KOREA: ANALYSTS ARE TRYING TO WORK OUT WHAT HAPPENS TO MARKETS IN THE EVENT OF AN ALL-OUT NUCLEAR WAR. (In case you’re wondering: in the event of a “potentially uncontained military conflict in which the global superpowers get involved,” the yield curve on Eurobonds would “likely flatten due to weaker risk appetite.”)12 Because this is so contrary to our nature, eventually even the U.S. political system will work its way back to some kind of balance. The Koch brothers may well have hit their zenith. Political scientists crunching the polling data said that the Kochs’ two signature laws (the attempted repeal of Obamacare and the successful tax “reform” package) were the “most unpopular pieces of major domestic legislation of the past quarter-century,” the journalist Michael Tomasky points out.


pages: 311 words: 99,699

Fool's Gold: How the Bold Dream of a Small Tribe at J.P. Morgan Was Corrupted by Wall Street Greed and Unleashed a Catastrophe by Gillian Tett

accounting loophole / creative accounting, asset-backed security, bank run, banking crisis, Black-Scholes formula, Blythe Masters, break the buck, Bretton Woods, business climate, business cycle, buy and hold, collateralized debt obligation, commoditize, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, easy for humans, difficult for computers, financial innovation, fixed income, housing crisis, interest rate derivative, interest rate swap, Kickstarter, locking in a profit, Long Term Capital Management, McMansion, money market fund, mortgage debt, North Sea oil, Northern Rock, Renaissance Technologies, risk tolerance, Robert Shiller, Robert Shiller, Satyajit Das, short selling, sovereign wealth fund, statistical model, The Great Moderation, too big to fail, value at risk, yield curve

Since 2004, the Fed had been steadily raising interest rates, along with the Bank of England, in a deliberate effort to prick the bubble. The European Central Bank had belatedly followed suit. Yet these moves hadn’t worked. Instead of rising, the cost of borrowing had stubbornly continued to fall in many corners of the market. In the US government bond sphere, yields on 10-year Treasuries even tumbled below short-term bond yields, creating a bizarre pattern known as an “inverted yield curve.” Alan Greenspan dubbed the situation a “conundrum.” There were other puzzles, too. In previous decades, the price of assets had been volatile when surprises hit the markets, be they an oil price shock, a rate rise, or a swing in the housing market. However, as Basel’s BIS noted at the time, the “striking feature of financial market behavior” in the twenty-first century was “the low level of price volatility over a wide range of financial assets and markets.”


pages: 350 words: 103,270

The Devil's Derivatives: The Untold Story of the Slick Traders and Hapless Regulators Who Almost Blew Up Wall Street . . . And Are Ready to Do It Again by Nicholas Dunbar

asset-backed security, bank run, banking crisis, Basel III, Black Swan, Black-Scholes formula, bonus culture, break the buck, buy and hold, capital asset pricing model, Carmen Reinhart, Cass Sunstein, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delayed gratification, diversification, Edmond Halley, facts on the ground, financial innovation, fixed income, George Akerlof, implied volatility, index fund, interest rate derivative, interest rate swap, Isaac Newton, John Meriwether, Kenneth Rogoff, Kickstarter, Long Term Capital Management, margin call, market bubble, money market fund, Myron Scholes, Nick Leeson, Northern Rock, offshore financial centre, Paul Samuelson, price mechanism, regulatory arbitrage, rent-seeking, Richard Thaler, risk tolerance, risk/return, Ronald Reagan, shareholder value, short selling, statistical model, The Chicago School, Thomas Bayes, time value of money, too big to fail, transaction costs, value at risk, Vanguard fund, yield curve, zero-sum game

Just how heavily traded these contracts became can be gauged from the total “notional” amount of debt that was supposed to be transformed by the swaps (which is not the same as their value): by June 2008, a staggering $356 trillion of interest rate swaps had been written, according to the Bank for International Settlements.2 As with forward contracts on currencies and commodities, the rates quoted on these swaps are considered to be a more informative way of comparing different borrowing timescales (the so-called yield curve) than the underlying government bonds or deposit rates themselves. Derivatives—at least the simplest, most popular forms of them—functioned best by being completely neutral in purpose. The contracts don’t say how you feel about the derivative and its underlying quantity. They don’t specify that you are a hate-to-lose-money corporate treasurer looking to reduce uncertainty in foreign exchange or commodities.


pages: 430 words: 109,064

13 Bankers: The Wall Street Takeover and the Next Financial Meltdown by Simon Johnson, James Kwak

American ideology, Andrei Shleifer, Asian financial crisis, asset-backed security, bank run, banking crisis, Bernie Madoff, Bonfire of the Vanities, bonus culture, break the buck, business cycle, buy and hold, capital controls, Carmen Reinhart, central bank independence, Charles Lindbergh, collapse of Lehman Brothers, collateralized debt obligation, commoditize, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, Edward Glaeser, Eugene Fama: efficient market hypothesis, financial deregulation, financial innovation, financial intermediation, financial repression, fixed income, George Akerlof, Gordon Gekko, greed is good, Home mortgage interest deduction, Hyman Minsky, income per capita, information asymmetry, interest rate derivative, interest rate swap, Kenneth Rogoff, laissez-faire capitalism, late fees, light touch regulation, Long Term Capital Management, market bubble, market fundamentalism, Martin Wolf, money market fund, moral hazard, mortgage tax deduction, Myron Scholes, Paul Samuelson, Ponzi scheme, price stability, profit maximization, race to the bottom, regulatory arbitrage, rent-seeking, Robert Bork, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, Satyajit Das, sovereign wealth fund, The Myth of the Rational Market, too big to fail, transaction costs, value at risk, yield curve

We did not realize they were already more like us than we cared to admit. * The Tenth Amendment, part of the Bill of Rights, was technically not yet in force, but by the end of 1790 it had been ratified by nine states out of the ten necessary. * A trust was a form of legal organization used to combine multiple companies into a single business entity. * Lowering short-term interest rates can also help banks by “steepening the yield curve.” Since banks typically borrow for short periods of time and lend for long periods of time, if short-term rates fall while long-term rates remain unchanged, their profit margin—the spread between long- and short-term rates—increases. * An alternative explanation, advanced by Barry Eichengreen and Peter Temin, is that the Federal Reserve was constrained by its adherence to the international gold standard; expanding the money supply would have caused a severe devaluation of the dollar.93 2 OTHER PEOPLE’S OLIGARCHS Financial institutions have priced risks poorly and have been willing to finance an excessively large portion of investment plans of the corporate sector, resulting in high leveraging.


pages: 417 words: 109,367

The End of Doom: Environmental Renewal in the Twenty-First Century by Ronald Bailey

3D printing, additive manufacturing, agricultural Revolution, Albert Einstein, Asilomar, autonomous vehicles, business cycle, Cass Sunstein, Climatic Research Unit, Commodity Super-Cycle, conceptual framework, corporate governance, creative destruction, credit crunch, David Attenborough, decarbonisation, dematerialisation, demographic transition, disruptive innovation, diversified portfolio, double helix, energy security, failed state, financial independence, Gary Taubes, hydraulic fracturing, income inequality, Induced demand, Intergovernmental Panel on Climate Change (IPCC), invisible hand, knowledge economy, meta analysis, meta-analysis, Naomi Klein, oil shale / tar sands, oil shock, pattern recognition, peak oil, Peter Calthorpe, phenotype, planetary scale, price stability, profit motive, purchasing power parity, race to the bottom, RAND corporation, rent-seeking, Stewart Brand, Tesla Model S, trade liberalization, University of East Anglia, uranium enrichment, women in the workforce, yield curve

“if during the next”: Paul Waggoner, “How Much Land Can Ten Billion Spare for Nature?” Jesse H. Ausubel and H. Dale Langford, eds., Technological Trajectories and the Human Environment, National Academy of Engineering, 1997, 56–73. www.nap.edu/openbook.php?record_id=4767&page=56. India produces 31 bushels: Ronald Phillips, “Mobilizing Science to Break Yield Barriers.” Background paper to the CGIAR 2009 Science Forum workshop: “Beyond the Yield Curve: Exerting the Power of Genetics, Genomics and Synthetic Biology,” “2009, 17. www.scienceforum2009.nl/Portals/11/BGWS4.pdf. that past population growth: Julio A. Gonzalo, Félix-Fernando Muñoz, David J. Santos, “Using a Rate Equations Approach to Model World Population Trends.” Simulation: Transactions of the Society for Modeling and Simulation International 89 (February 2013): 192–198. “Overpopulation was a spectre”: “A Model Predicts That the World’s Populations Will Stop Growing in 2050.”


Capital Ideas Evolving by Peter L. Bernstein

Albert Einstein, algorithmic trading, Andrei Shleifer, asset allocation, business cycle, buy and hold, buy low sell high, capital asset pricing model, commodity trading advisor, computerized trading, creative destruction, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, diversification, diversified portfolio, endowment effect, equity premium, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, high net worth, hiring and firing, index fund, invisible hand, Isaac Newton, John Meriwether, John von Neumann, Joseph Schumpeter, Kenneth Arrow, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, market bubble, mental accounting, money market fund, Myron Scholes, paper trading, passive investing, Paul Samuelson, price anchoring, price stability, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, statistical model, survivorship bias, systematic trading, technology bubble, The Wealth of Nations by Adam Smith, transaction costs, yield curve, Yogi Berra, zero-sum game

“Can Mutual Fund ‘Stars’ Really Pick Stocks? New Evidence from a Bootstrap Analysis,” Journal of Finance, Vol. 61, No. 6 ( December). Kritzman, Mark, and Lee Thomas, 2004. “Re-Engineering Investment Management,” The Journal of Portfolio Management, 30th Anniversary Issue (September), pp. 70 –79. Kroszner, Randall, 2006. “The Conquest of Worldwide Inf lation: Currency Competition and Its Implications for Interest Rates and the Yield Curve,” Cato Monetary Policy Conference, November 16. Kurz, Mordecai, 1994. “On the Structure and Diversity of Rational Beliefs,” Economic Theory, Springer-Verlag, Vol. 4, pp. 877–900. Kurz, Mordecai, ed., 1997. Endogenous Economic Fluctuations: Studies in the Theory of Rational Beliefs, Springer Series in Economic Theory, No. 6 (August), Springer-Verlag. Kurz, Mordecai, M. Jin, and M. Motolese, 2005.


pages: 404 words: 113,514

Atrocity Archives by Stross, Charles

airport security, anthropic principle, Berlin Wall, brain emulation, British Empire, Buckminster Fuller, defense in depth, disintermediation, experimental subject, glass ceiling, haute cuisine, hypertext link, Khyber Pass, mandelbrot fractal, Menlo Park, MITM: man-in-the-middle, NP-complete, the medium is the message, Y2K, yield curve

What yield have you set it to?" I ask. Howe raises an eyebrow. "Tell him," says Alan. "It's a selective yield gadget," says Howe. "We can set it to anything from fifteen kilotons to a quarter of a megaton--it's a mechanical process, screw jacks adjust the gap between the fusion sparkplug and the initiator charge so that we get more or less fusion output. Right now it's at the upper end of the yield curve, dialled all the way up to city-buster size. What's this got to do with anything?" "Well." I lick my lips; it's really cold in here now and my breath is steaming. "To open a gate big enough to bring through a large creature like whatever ate this universe takes a whole lot of entropy. The Ahnenerbe did it in this universe by ritually murdering roughly ten million people: information destruction increases entropy.


pages: 479 words: 113,510

Fed Up: An Insider's Take on Why the Federal Reserve Is Bad for America by Danielle Dimartino Booth

Affordable Care Act / Obamacare, asset-backed security, bank run, barriers to entry, Basel III, Bernie Sanders, break the buck, Bretton Woods, business cycle, central bank independence, collateralized debt obligation, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, financial deregulation, financial innovation, fixed income, Flash crash, forward guidance, full employment, George Akerlof, greed is good, high net worth, housing crisis, income inequality, index fund, inflation targeting, interest rate swap, invisible hand, John Meriwether, Joseph Schumpeter, liquidity trap, London Whale, Long Term Capital Management, margin call, market bubble, Mexican peso crisis / tequila crisis, money market fund, moral hazard, Myron Scholes, natural language processing, negative equity, new economy, Northern Rock, obamacare, price stability, pushing on a string, quantitative easing, regulatory arbitrage, Robert Shiller, Robert Shiller, Ronald Reagan, selection bias, short selling, side project, Silicon Valley, The Great Moderation, The Wealth of Nations by Adam Smith, too big to fail, trickle-down economics, yield curve

The combination of Dodd-Frank, with its aim of limiting risk-taking, and Basel III, with its increased capital requirements, had proved to be a toxic combination. Was it any wonder that commercial bank officers were stumped? The stated aim of QE was to encourage banks to loan money. But other rules required they hold more capital against fresh loans if they did—to say nothing of the risk they assumed if yield curve normality ever made a comeback. Private equity kingpins had become the new overlords of the corporate bond market. They had also discovered the profitability of being landlords. Money was cheap for those who could access it. And investors were eager to buy into the next private equity fund in the hopes they could eke out positive returns. So private equity set its sights on the “sand states,” ground zero of the housing crisis.


pages: 409 words: 118,448

An Extraordinary Time: The End of the Postwar Boom and the Return of the Ordinary Economy by Marc Levinson

affirmative action, airline deregulation, banking crisis, Big bang: deregulation of the City of London, Boycotts of Israel, Bretton Woods, business cycle, Capital in the Twenty-First Century by Thomas Piketty, car-free, Carmen Reinhart, central bank independence, centre right, clean water, deindustrialization, endogenous growth, falling living standards, financial deregulation, floating exchange rates, full employment, George Gilder, Gini coefficient, global supply chain, income inequality, income per capita, indoor plumbing, informal economy, intermodal, invisible hand, Kenneth Rogoff, knowledge economy, late capitalism, linear programming, manufacturing employment, new economy, Nixon shock, North Sea oil, oil shock, Paul Samuelson, pension reform, price stability, purchasing power parity, refrigerator car, Right to Buy, rising living standards, Robert Gordon, rolodex, Ronald Coase, Ronald Reagan, Simon Kuznets, statistical model, strikebreaker, structural adjustment programs, The Rise and Fall of American Growth, Thomas Malthus, total factor productivity, unorthodox policies, upwardly mobile, War on Poverty, Washington Consensus, Winter of Discontent, Wolfgang Streeck, women in the workforce, working-age population, yield curve, Yom Kippur War, zero-sum game

Although Carter was able to push Burns out in January 1978, by then inflation was climbing back toward the double digits. As the Fed began raising overnight interest rates aggressively to clamp down on inflation, interest rates on the Treasury’s short-term bonds rose close to those on its long-term bonds. On August 18, 1978, the lines crossed: investors earned more for lending to the government for two years than for ten. That unusual condition, known in the financial markets as an inverted yield curve, was an alarm bell, an unmistakable warning that a recession was likely in the second half of 1979. And then came the second oil crisis, driven by the revolution in Iran and a decision by Saudi Arabia to limit oil production. After holding steady since 1974, the average cost of a barrel of crude doubled over the course of 1979. A more relevant figure for American voters, the price of gasoline, went from seventy cents per gallon to $1.11, and buying it often required a lengthy wait in line.


pages: 393 words: 115,263

Planet Ponzi by Mitch Feierstein

Affordable Care Act / Obamacare, Albert Einstein, Asian financial crisis, asset-backed security, bank run, banking crisis, barriers to entry, Bernie Madoff, break the buck, centre right, collapse of Lehman Brothers, collateralized debt obligation, commoditize, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, disintermediation, diversification, Donald Trump, energy security, eurozone crisis, financial innovation, financial intermediation, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, high net worth, High speed trading, illegal immigration, income inequality, interest rate swap, invention of agriculture, light touch regulation, Long Term Capital Management, low earth orbit, mega-rich, money market fund, moral hazard, mortgage debt, negative equity, Northern Rock, obamacare, offshore financial centre, oil shock, pensions crisis, plutocrats, Plutocrats, Ponzi scheme, price anchoring, price stability, purchasing power parity, quantitative easing, risk tolerance, Robert Shiller, Robert Shiller, Ronald Reagan, too big to fail, trickle-down economics, value at risk, yield curve

You’d want to cover your manipulation in plenty of complicated talk about statistics, but the talk wouldn’t signify a string bean. The second thing you’d want to do is to start churning out new dollar bills. You’d print like crazy. You wouldn’t talk about trashing the currency, of course; you’d talk about price stability, about quantitative easing, about Operation Twist and bringing down the long end of the yield curve. Ideally, too, you’d have someone in charge who really believed in the value of what he was doing, someone who didn’t really live in the real world. Maybe a professor of something. A guy who had studied a period of history from eighty years ago and who’s been yearning all his life to save the world using techniques which might or might not have worked back then, but which certainly don’t make sense in the present day.


pages: 374 words: 114,600

The Quants by Scott Patterson

Albert Einstein, asset allocation, automated trading system, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Swan, Black-Scholes formula, Blythe Masters, Bonfire of the Vanities, Brownian motion, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, Gordon Gekko, greed is good, Haight Ashbury, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, index fund, invention of the telegraph, invisible hand, Isaac Newton, job automation, John Meriwether, John Nash: game theory, Kickstarter, law of one price, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, merger arbitrage, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, Ponzi scheme, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, Robert Mercer, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Sergey Aleynikov, short selling, South Sea Bubble, speech recognition, statistical arbitrage, The Chicago School, The Great Moderation, The Predators' Ball, too big to fail, transaction costs, value at risk, volatility smile, yield curve, éminence grise

In January 1992, he received a call from Pimco, the West Coast bond manager run by Bill Gross. A billionaire former blackjack card counter (in college he’d devoured Beat the Dealer and Beat the Market), Gross religiously applied his gambling acumen to his investment decisions on a daily basis. Pimco had gotten hold of Asness’s first published research, “OAS Models, Expected Returns, and a Steep Yield Curve,” and was interested in recruiting him. Over the course of the year, Asness had several interviews with Pimco. In 1993, the company offered him a job building quantitative models and tools. It was an ideal position, Asness thought, combining the research side of academia with the applied rigor of Wall Street. Goldman, upon learning about the offer, offered him a similar job at GSAM. Asness took it, reasoning that Goldman was closer to home in Roslyn Heights.


pages: 490 words: 117,629

Unconventional Success: A Fundamental Approach to Personal Investment by David F. Swensen

asset allocation, asset-backed security, buy and hold, capital controls, cognitive dissonance, corporate governance, diversification, diversified portfolio, fixed income, index fund, law of one price, Long Term Capital Management, market bubble, market clearing, market fundamentalism, money market fund, passive investing, Paul Samuelson, pez dispenser, price mechanism, profit maximization, profit motive, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, shareholder value, Silicon Valley, Steve Ballmer, stocks for the long run, survivorship bias, technology bubble, the market place, transaction costs, Vanguard fund, yield curve, zero-sum game

If tax rates change, the value of the tax exemption for municipal bonds changes too. A reduction in tax rates reduces the value of the tax exemption and vice versa. If Congress limits or eliminates the tax exemption, the values of municipal bonds would decline. Legislative uncertainty contributes to higher-than-expected long-term tax-exempt yields. Proving that generalizations invite exceptions, sometimes market forces fail to work on the short end of the yield curve. Consider yields for Vanguard’s money-market offerings. In September 2004, the tax-exempt money fund yield matched the taxable fund yield. A top-marginal-bracket taxpayer benefited to the tune of 0.4 percent on an after-tax basis by choosing the tax-exempt fund. The early-September yields represented more than a passing opportunity. For the year prior to August 31, 2004, Vanguard’s tax-exempt fund produced a yield of 0.9 percent, materially higher than the taxable fund’s yield of 0.8 percent.


pages: 466 words: 127,728

The Death of Money: The Coming Collapse of the International Monetary System by James Rickards

Affordable Care Act / Obamacare, Asian financial crisis, asset allocation, Ayatollah Khomeini, bank run, banking crisis, Ben Bernanke: helicopter money, bitcoin, Black Swan, Bretton Woods, BRICs, business climate, business cycle, buy and hold, capital controls, Carmen Reinhart, central bank independence, centre right, collateralized debt obligation, collective bargaining, complexity theory, computer age, credit crunch, currency peg, David Graeber, debt deflation, Deng Xiaoping, diversification, Edward Snowden, eurozone crisis, fiat currency, financial innovation, financial intermediation, financial repression, fixed income, Flash crash, floating exchange rates, forward guidance, G4S, George Akerlof, global reserve currency, global supply chain, Growth in a Time of Debt, income inequality, inflation targeting, information asymmetry, invisible hand, jitney, John Meriwether, Kenneth Rogoff, labor-force participation, Lao Tzu, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market clearing, market design, money market fund, money: store of value / unit of account / medium of exchange, mutually assured destruction, obamacare, offshore financial centre, oil shale / tar sands, open economy, plutocrats, Plutocrats, Ponzi scheme, price stability, quantitative easing, RAND corporation, reserve currency, risk-adjusted returns, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Satoshi Nakamoto, Silicon Valley, Silicon Valley startup, Skype, sovereign wealth fund, special drawing rights, Stuxnet, The Market for Lemons, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, too big to fail, trade route, undersea cable, uranium enrichment, Washington Consensus, working-age population, yield curve

Corporations in the EU are predominantly taxed on a national basis, meaning tax is paid to a host country only based on profits made in that country, which contrasts favorably with the U.S. system of global taxation, in which a U.S. corporation pays tax on foreign as well as domestic profits. Both the EU and the United States have managed to maintain low inflation in recent years, but Europe has done so with significantly less money printing and yield-curve manipulation, which means its potential for future inflation based on changes in the turnover or velocity of money is reduced. In contrast, China has had a persistent problem with inflation due to Chinese efforts to absorb Federal Reserve money printing to maintain a peg between the yuan and the dollar. Of the three largest economic zones, the EU has the best track record on inflation both in terms of recent experience and prospects going forward.


pages: 457 words: 143,967

The Bank That Lived a Little: Barclays in the Age of the Very Free Market by Philip Augar

activist fund / activist shareholder / activist investor, Asian financial crisis, asset-backed security, bank run, banking crisis, Big bang: deregulation of the City of London, Bonfire of the Vanities, bonus culture, break the buck, call centre, collateralized debt obligation, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, family office, financial deregulation, financial innovation, fixed income, high net worth, hiring and firing, index card, index fund, interest rate derivative, light touch regulation, loadsamoney, Long Term Capital Management, Martin Wolf, money market fund, moral hazard, Nick Leeson, Northern Rock, offshore financial centre, old-boy network, out of africa, prediction markets, quantitative easing, Ronald Reagan, shareholder value, short selling, Sloane Ranger, Social Responsibility of Business Is to Increase Its Profits, sovereign wealth fund, too big to fail, wikimedia commons, yield curve

It was a presentation headed ‘Interest Rate Risk Management, Corporate Risk Advisory, Barclays Capital’. She worked through it, talking about derivatives and hedges, things she said financial institutions were doing all the time but which were new to Edwards. She showed him graphs and used terms he had never heard of like ‘structured collar cap with a double floor’, ‘mark-to-market’, ‘upward sloping yield curve’ and ‘forward rates’. The presentation included a page which showed the corporate logos of companies which used such products, for example Citigroup, Goldman Sachs and HBOS, and Carol said that Barclays Capital had produced a simplified version for smaller companies. The pack included a page asking ‘Which should I choose? Interest rate cap? Interest rate collar? Structured collar?’ Each option was followed by a step-by-step guide: ‘Choose this strategy if you think …’ and then a series of interest rate scenarios.


pages: 611 words: 130,419

Narrative Economics: How Stories Go Viral and Drive Major Economic Events by Robert J. Shiller

agricultural Revolution, Albert Einstein, algorithmic trading, Andrei Shleifer, autonomous vehicles, bank run, banking crisis, basic income, bitcoin, blockchain, business cycle, butterfly effect, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, central bank independence, collective bargaining, computerized trading, corporate raider, correlation does not imply causation, cryptocurrency, Daniel Kahneman / Amos Tversky, debt deflation, disintermediation, Donald Trump, Edmond Halley, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, full employment, George Akerlof, germ theory of disease, German hyperinflation, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, implied volatility, income inequality, inflation targeting, invention of radio, invention of the telegraph, Jean Tirole, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, litecoin, market bubble, money market fund, moral hazard, Northern Rock, nudge unit, Own Your Own Home, Paul Samuelson, Philip Mirowski, plutocrats, Plutocrats, Ponzi scheme, publish or perish, random walk, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, Rubik’s Cube, Satoshi Nakamoto, secular stagnation, shareholder value, Silicon Valley, speech recognition, Steve Jobs, Steven Pinker, stochastic process, stocks for the long run, superstar cities, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, theory of mind, Thorstein Veblen, traveling salesman, trickle-down economics, tulip mania, universal basic income, Watson beat the top human players on Jeopardy!, We are the 99%, yellow journalism, yield curve, Yom Kippur War

Rubin, David C. 1997. Memory in Oral Traditions: The Cognitive Psychology of Epic, Ballads, and Counting-Out Rhymes. Oxford: Oxford University Press. Rubinstein Mark, and Hayne Leland H. 1981. “Replicating Options with Positions in Stock and Cash.” Financial Analysts Journal 37(4):63–72. Rudebusch, Glenn D., and John C. Williams. 2009. “Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve.” Journal of Business and Economic Statistics 27(4):492–503. Saavedra, Javier, Mercedes Cubero, and Paul Crawford. 2009. “Incomprehensibility in the Narratives of Individuals with a Diagnosis of Schizophrenia.” Qualitative Health Research 19(11):1548. Saiz, Albert. 2010. “The Geographic Determinants of Housing Supply.” Quarterly Journal of Economics 125(3):1253–96. Sala-i-Martin, Xavier. 2006.


pages: 1,242 words: 317,903

The Man Who Knew: The Life and Times of Alan Greenspan by Sebastian Mallaby

"Robert Solow", airline deregulation, airport security, Andrei Shleifer, anti-communist, Asian financial crisis, balance sheet recession, bank run, barriers to entry, Benoit Mandelbrot, Bretton Woods, business cycle, central bank independence, centralized clearinghouse, collateralized debt obligation, conceptual framework, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, energy security, equity premium, fiat currency, financial deregulation, financial innovation, fixed income, Flash crash, forward guidance, full employment, Hyman Minsky, inflation targeting, information asymmetry, interest rate swap, inventory management, invisible hand, Kenneth Rogoff, Kickstarter, Kitchen Debate, laissez-faire capitalism, Long Term Capital Management, low skilled workers, market bubble, market clearing, Martin Wolf, money market fund, moral hazard, mortgage debt, Myron Scholes, new economy, Nixon shock, Northern Rock, paper trading, paradox of thrift, Paul Samuelson, plutocrats, Plutocrats, popular capitalism, price stability, RAND corporation, rent-seeking, Robert Shiller, Robert Shiller, rolodex, Ronald Reagan, Saturday Night Live, savings glut, secular stagnation, short selling, The Great Moderation, the payments system, The Wealth of Nations by Adam Smith, too big to fail, trade liberalization, unorthodox policies, upwardly mobile, WikiLeaks, women in the workforce, Y2K, yield curve, zero-sum game

They make us more vulnerable to the buildup of distortions in financial markets that can only be unwound with some drama.” “Bankers are willing to take on more risk than I have heard them admit to in recent years,” added Governor Mark Olson, who was himself a former banker. Vice Chairman Roger Ferguson pushed the argument further, linking the financial exuberance with the Fed’s forward guidance about future interest rates. “Perhaps we are anchoring the yield curve more than we’d like,” he suggested. “The fixed-income markets in particular are not in fact doing the appropriate job of pricing risks.” He was suggesting that the Fed’s forward guidance had made life too predictable, lulling speculators into complacency. “We need in some sense to remove the anchor that we have placed on those markets,” he concluded. Greenspan also sounded worried. “It sounds as though we’re back in the late ’90s or perhaps early 2000,” he said, recalling the extremes of the tech bubble.

If house prices did turn out to constitute a bubble, they could always clean up afterward. The danger of a snapback had to be weighed against more certain and immediate goals: to allow as much growth and employment as possible, consistent with a stable consumer-price index.45 • • • For the next few months, the Fed continued to guide investors about its future policy, oblivious to Roger Ferguson’s warning that this might “anchor” the yield curve and lull investors into complacency. In March 2004 the FOMC repeated its promise to be patient about raising interest rates; in May it declared it would tighten “at a pace that is likely to be measured.” It was only in June, after fully twelve months with a 1 percent federal funds rate, that the FOMC ventured a quarter-point hike. By this point, house prices had risen by 20 percent in a year.46 It was the same amount that they had gained during the entire decade of the 1990s.


pages: 590 words: 153,208

Wealth and Poverty: A New Edition for the Twenty-First Century by George Gilder

"Robert Solow", affirmative action, Albert Einstein, Bernie Madoff, British Empire, business cycle, capital controls, cleantech, cloud computing, collateralized debt obligation, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, deindustrialization, diversified portfolio, Donald Trump, equal pay for equal work, floating exchange rates, full employment, George Gilder, Gunnar Myrdal, Home mortgage interest deduction, Howard Zinn, income inequality, invisible hand, Jane Jacobs, Jeff Bezos, job automation, job-hopping, Joseph Schumpeter, knowledge economy, labor-force participation, longitudinal study, margin call, Mark Zuckerberg, means of production, medical malpractice, minimum wage unemployment, money market fund, money: store of value / unit of account / medium of exchange, Mont Pelerin Society, moral hazard, mortgage debt, non-fiction novel, North Sea oil, paradox of thrift, Paul Samuelson, plutocrats, Plutocrats, Ponzi scheme, post-industrial society, price stability, Ralph Nader, rent control, Robert Gordon, Ronald Reagan, Silicon Valley, Simon Kuznets, skunkworks, Steve Jobs, The Wealth of Nations by Adam Smith, Thomas L Friedman, upwardly mobile, urban renewal, volatility arbitrage, War on Poverty, women in the workforce, working poor, working-age population, yield curve, zero-sum game

PROLOGUE THE SECRET OF ENTERPRISE THIRTY YEARS AFTER THE publication of Wealth & Poverty, shaken by a global financial fiasco, I find myself buckling down to engage once again these central themes of human life and economics. Back during the tempestuous late years of the 1970s, with President Jimmy Carter waving limp white flags of national malaise, with hostages still held in Tehran, petroleum and gold prices shrilling doom-laden alarms, U.S. banks gasping for capital down a ferociously inverted yield curve (borrowing dear in short-term markets and lending low in long-term bonds and mortgages), with the Soviet Union battening rich on oil wealth, and John Kenneth Galbraith joining the CIA in acclaiming the robustness and fast growth of the “astonishing” Soviet economy, I declared that socialism was dead. This fact, I acknowledged, remained unrequited by a comparable triumph of capitalism. Even Irving Kristol, then the most eloquent and sophisticated defender of enterprise, could only muster Two Cheers for Capitalism.


pages: 636 words: 140,406

The Case Against Education: Why the Education System Is a Waste of Time and Money by Bryan Caplan

affirmative action, Affordable Care Act / Obamacare, assortative mating, conceptual framework, correlation does not imply causation, deliberate practice, deskilling, disruptive innovation, en.wikipedia.org, endogenous growth, experimental subject, fear of failure, Flynn Effect, future of work, George Akerlof, ghettoisation, hive mind, job satisfaction, Kenneth Arrow, Khan Academy, labor-force participation, longitudinal study, low skilled workers, market bubble, mass incarceration, meta analysis, meta-analysis, Peter Thiel, price discrimination, profit maximization, publication bias, risk tolerance, Robert Gordon, Ronald Coase, school choice, selection bias, Silicon Valley, statistical model, Steven Pinker, The Bell Curve by Richard Herrnstein and Charles Murray, the scientific method, The Wisdom of Crowds, trickle-down economics, twin studies, unpaid internship, upwardly mobile, women in the workforce, yield curve, zero-sum game

. ———. 2013. “Child Labor Bulletin 101: Child Labor Provisions for Nonagricultural Occupations.” Accessed August 18, 2015. http://www.dol.gov/whd/regs/compliance/childlabor101.pdf. ———. 2014. “State Unemployment Insurance Benefits.” Last modified June 3. http://workforcesecurity.doleta.gov/unemploy/uifactsheet.asp. United States Department of the Treasury. 2016. “Resource Center: Daily Treasury Yield Curve Rates.” Accessed March 30, 2016. https://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield. United States Equal Opportunity Commission. 2015. “Prohibited Employment Policies/Practices.” Accessed February 28, 2015. http://www.eeoc.gov/laws/practices/index.cfm. University of California Berkeley. 2015a. “Berkeley Economics: Major Requirements.” Accessed December 2, 2015. https://www.econ.berkeley.edu/undergrad/current/major-requirements. ———. 2015b.


pages: 516 words: 157,437

Principles: Life and Work by Ray Dalio

Albert Einstein, asset allocation, autonomous vehicles, backtesting, cognitive bias, Deng Xiaoping, diversification, Elon Musk, follow your passion, hiring and firing, iterative process, Jeff Bezos, Long Term Capital Management, margin call, microcredit, oil shock, performance metric, planetary scale, quantitative easing, risk tolerance, Ronald Reagan, Silicon Valley, Steve Jobs, transaction costs, yield curve

He’d been wanting to get his wealth out of paper money for the past few years, so he’d been buying commodities, especially silver, which he had started purchasing for about $1.29 per ounce, as a hedge against inflation. He kept buying and buying as inflation and the price of silver went up, until he had essentially cornered the silver market. At that point, silver was trading at around $10. I told him I thought it might be a good time to get out because the Fed was becoming tight enough to raise short-term interest rates above long-term rates (which was called “inverting the yield curve”). Every time that happened, inflation-hedged assets and the economy went down. But Bunker was in the oil business, and the Middle East oil producers he talked to were still worried about the depreciation of the dollar. They had told him they were also going to buy silver as a hedge against inflation so he held on to it in the expectation that its price would continue to rise. I got out. On December 8, 1979, Barbara and I had our second son, Paul.


pages: 526 words: 158,913

Crash of the Titans: Greed, Hubris, the Fall of Merrill Lynch, and the Near-Collapse of Bank of America by Greg Farrell

Airbus A320, Apple's 1984 Super Bowl advert, bank run, banking crisis, bonus culture, call centre, Captain Sullenberger Hudson, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, financial innovation, fixed income, glass ceiling, high net worth, Long Term Capital Management, mass affluent, Mexican peso crisis / tequila crisis, Nelson Mandela, plutocrats, Plutocrats, Ronald Reagan, six sigma, sovereign wealth fund, technology bubble, too big to fail, US Airways Flight 1549, yield curve

The idea was simple enough, to package mortgages of various durations and interest rates into tranches, then securitize those tranches and sell them like bonds, where buyers could look forward to receiving annual payments, or “coupons,” on their investment. Thain immersed himself in the business, learning the arcane details of mortgage trading, from the payment cycles and coupon rates to the special considerations of prepayment pools and the “negative convexity”—an inversion of the standard price/yield curve—that creeps into the valuations of mortgage portfolios. Mortgage trading fell within Goldman’s fixed-income trading division, and the leader of that business, Jon Corzine—who would eventually be elected U.S. senator from New Jersey and governor of the Garden State—took a special interest in Thain, a fellow native of Illinois. At Goldman Sachs, which employed only the best and brightest financial minds trained at the top business schools, everyone is smart, so a partner’s rise in the organization comes from how much revenue he generates for the firm and whether one of the firm’s top partners takes an interest in him.


pages: 442 words: 39,064

Why Stock Markets Crash: Critical Events in Complex Financial Systems by Didier Sornette

Asian financial crisis, asset allocation, Berlin Wall, Bretton Woods, Brownian motion, business cycle, buy and hold, capital asset pricing model, capital controls, continuous double auction, currency peg, Deng Xiaoping, discrete time, diversified portfolio, Elliott wave, Erdős number, experimental economics, financial innovation, floating exchange rates, frictionless, frictionless market, full employment, global village, implied volatility, index fund, information asymmetry, intangible asset, invisible hand, John von Neumann, joint-stock company, law of one price, Louis Bachelier, mandelbrot fractal, margin call, market bubble, market clearing, market design, market fundamentalism, mental accounting, moral hazard, Network effects, new economy, oil shock, open economy, pattern recognition, Paul Erdős, Paul Samuelson, quantitative trading / quantitative finance, random walk, risk/return, Ronald Reagan, Schrödinger's Cat, selection bias, short selling, Silicon Valley, South Sea Bubble, statistical model, stochastic process, stocks for the long run, Tacoma Narrows Bridge, technological singularity, The Coming Technological Singularity, The Wealth of Nations by Adam Smith, Tobin tax, total factor productivity, transaction costs, tulip mania, VA Linux, Y2K, yield curve

GDP at 7% growth rate Asia up P/Sales new metric big jobs number NAV angst Bull market forever? Insults Back into cave 8 week bear market!? Disbelief Looking good! B2C Greenspan speaks Optimistic CNBC guest Pain Joe Ignore history Arggh! Larry Nothing matters Any gains lost in next day rally Ralph Bad breadth Wealth effect Abby Earnings slowdown Big volume Futures up Greenspan silent Rally!!! Bears bail Phew! MSFT breakup e-broker TV ads P/E’s of 2000 Weird yield curve Buy and hold forever “Bottom is in” Mergers Soros out Gold auctions Margin call W$W elves 401k inflows 16 year olds beat market vets Flight to safety Hot market Dollar goes every which way Old Economy New Economy Oil up DOW 36,000 IPO billionaires 30yr bond extinct Fig. 4.1. Cartoon illustrating the many factors influencing traders, as well as the psychological and social nature of the investment universe (source: anonymous).


pages: 586 words: 160,321