25 results back to index
barriers to entry, conceptual framework, correlation coefficient, discrete time, disintermediation, distributed generation, experimental economics, financial intermediation, index arbitrage, interest rate swap, inventory management, market clearing, market design, market friction, market microstructure, martingale, price discovery process, price discrimination, quantitative trading / quantitative ﬁnance, random walk, Richard Thaler, second-price auction, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, two-sided market, ultimatum game
Introduction Trading Mechanisms The Roll Model of Trade Prices Univariate Time-Series Analysis Sequential Trade Models Order Flow and the Probability of Informed Trading Strategic Trade Models A Generalized Roll Model Multivariate Linear Microstructure Models Multiple Securities and Multiple Prices Dealers and Their Inventories Limit Order Markets Depth Trading Costs: Retrospective and Comparative Prospective Trading Costs and Execution Strategies 3 9 23 31 42 56 61 67 78 94 106 118 131 143 153 Appendix: U.S. Equity Markets 166 Notes 179 References 183 Index 196 ix This page intentionally left blank Empirical Market Microstructure This page intentionally left blank 1 Introduction 1.1 Overview Market microstructure is the study of the trading mechanisms used for financial securities. There is no “microstructure manifesto,” and historical antecedents to the field can probably be found going back to the beginning of written language, but at some point, the field acquired a distinct identity. As good a starting point as any is the coinage of the term market microstructure in the paper of the same title by Garman (1976): We depart from the usual approaches of the theory of exchange by (1) making the assumption of asynchronous, temporally discrete market activities on the part of market agents and (2) adopting a viewpoint which treats the temporal microstructure, i.e., moment-to-moment aggregate exchange behavior, as an important descriptive aspect of such markets.
Amihud, Yakov, and Haim Mendelson, 1987, Trading mechanisms and stock returns: An empirical investigation, Journal of Finance 42, 533–53. Amihud, Yakov, and Haim Mendelson, 1991. Market microstructure and price discovery on the Tokyo Stock Exchange, in William T. Ziemba, Warren Bailley, and Yasushi Hamao, eds., Japanese Financial Market Research, Contributions to Economic Analysis, no. 205 (North Holland, Amsterdam). Amihud, Yakov, Haim Mendelson, and Maurizio Murgia, 1990, Stock market microstructure and return volatility: Evidence from Italy, Journal of Banking and Finance 14, 423–40. Amihud, Yakov, Haim Mendelson, and Lasse Heje Pedersen, 2005, Market microstructure and asset pricing (Stern School, New York University). 183 184 REFERENCES Angel, James J., 1994, Limit versus market orders (School of Business Administration, Georgetown University).
Empirical Market Microstructure This page intentionally left blank Empirical Market Microstructure The Institutions, Economics, and Econometrics of Securities Trading Joel Hasbrouck 1 2007 1 Oxford University Press, Inc., publishes works that further Oxford University’s objective of excellence in research, scholarship, and education. Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Copyright © 2007 by Oxford University Press Published by Oxford University Press, Inc. 198 Madison Avenue, New York, New York 10016 www.oup.com Oxford is a registered trademark of Oxford University Press All rights reserved.
algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business process, capital asset pricing model, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, diversification, equity premium, fault tolerance, financial intermediation, fixed income, high net worth, implied volatility, index arbitrage, interest rate swap, inventory management, law of one price, Long Term Capital Management, Louis Bachelier, margin call, market friction, market microstructure, martingale, 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
Trading on market microstructure is the holy grail R 127 128 HIGH-FREQUENCY TRADING Time (GMT) 7:00:01 6:00:01 5:00:01 4:00:01 3:00:01 1.094 1.0935 1.093 1.0925 1.092 1.0915 1.091 1.0905 1.09 1.0895 1.089 2:00:01 USD/CHF Price Level Price Adjustment Period News release time News Release Time 06:42:20:491 06:38:42:450 06:35:48:931 06:31:40:095 06:27:16:752 06:22:56:927 06:17:57:625 06:14:39:905 06:11:22:955 06:08:00:711 06:05:30:391 05:58:53:276 05:51:56:382 05:45:29:923 05:35:09:464 05:28:12:164 05:14:09:406 05:00:22:013 04:51:44:015 04:44:41:972 04:31:40:802 Bid Ask 04:19:53:737 USD/CHF Price Level Price Adjustment Period 1.095 1.094 1.093 1.092 1.091 1.09 1.089 1.088 1.087 1.086 1.085 Time FIGURE 10.1 USD/CHF price adjustments to Swiss unemployment news, recorded on July 8, 2009 at hourly (top panel) and tick-by-tick (bottom panel) frequencies. of high-frequency trading. The idea of market microstructure trading is to extract information from the observable quote data and trade upon that extracted information in order to obtain gains. Holding periods for positions in market microstructure trading can vary in duration from seconds to hours. The optimal holding period is influenced by the transaction costs faced by the trader. A gross average gain for a position held just several seconds will likely be in the range of several basis points (1 basis point = 1 bp = 1 pip = 0.01%), at most. To make such trading viable, the expected gain has Trading on Market Microstructure 129 to surpass the transaction costs. In an institutional setting (e.g., on a proprietary trading desk of a broker-dealer), a trader will often face transaction costs of 1 bp or less on selected securities, making a seconds-based trading strategy with an expected gain of at least 2 bps per trade quite profitable.
., 274, 275, 277, 281, 292–293, 298 Management fees, 32 Margin call close order, 70 Market-aggressiveness selection, 274, 275–276 Market breadth, 62 Market depth, 62, 133 Market efficiency: predictability and, 78–79 profit opportunities and, 75–78 testing for, 79–89 MarketFactory, 25 Market impact costs, 290–293 Market microstructure trading, 4, 127–128 Market microstructure trading, information models, 129, 145–164 asymmetric information measures, 146–148 INDEX bid-ask spreads, 149–157 order aggressiveness, 157–160 order flow, 160–163 Market microstructure trading, inventory models, 127–143 liquidity provision, 133–134, 139–143 order types, 130–131 overview, 129–130 price adjustments, 127–128 profitable market making problems, 134–139 trader types, 131–133 Market-neutral arbitrage, 192–195 Market orders, versus limit orders, 61–63 Market participants, 24–26 Market resilience, inventory trading, 133 Market risk, 252, 253 hedging and, 269–270 measuring of, 254–260 stop losses and, 266 Markov switching models, 110–111 Markowitz, Harry, 202, 209, 213, 214, 295 Mark to market, risk measurement and, 263 Martell, Terrence, 158–159 Martingale hypothesis, market efficiency tests based on, 86–88 MatLab, 25 Maximum drawdown, 50–51 McQueen, Grant V., 179 Mean absolute deviation (MAD), 220–221 Mean absolute percentage error (MAPE), 221 Mean-reversion.
HG4529.A43 2010 332.64–dc22 2009029276 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 To my family Contents Acknowledgments xi CHAPTER 1 Introduction 1 CHAPTER 2 Evolution of High-Frequency Trading 7 Financial Markets and Technological Innovation Evolution of Trading Methodology CHAPTER 3 Overview of the Business of High-Frequency Trading 7 13 21 Comparison with Traditional Approaches to Trading 22 Market Participants 24 Operating Model 26 Economics 32 Capitalizing a High-Frequency Trading Business 34 Conclusion 35 CHAPTER 4 Financial Markets Suitable for High-Frequency Trading 37 Financial Markets and Their Suitability for High-Frequency Trading Conclusion 38 47 v vi CHAPTER 5 CONTENTS Evaluating Performance of High-Frequency Strategies 49 Basic Return Characteristics 49 Comparative Ratios 51 Performance Attribution 57 Other Considerations in Strategy Evaluation 58 Conclusion 60 CHAPTER 6 Orders, Traders, and Their Applicability to High-Frequency Trading 61 Order Types 61 Order Distributions 70 Conclusion 73 CHAPTER 7 Market Inefficiency and Profit Opportunities at Different Frequencies 75 Predictability of Price Moves at High Frequencies 78 Conclusion 89 CHAPTER 8 Searching for High-Frequency Trading Opportunities 91 Statistical Properties of Returns 91 Linear Econometric Models 97 Volatility Modeling 102 Nonlinear Models 108 Conclusion 114 CHAPTER 9 Working with Tick Data 115 Properties of Tick Data 116 Quantity and Quality of Tick Data 117 Bid-Ask Spreads 118 Contents vii Bid-Ask Bounce 120 Modeling Arrivals of Tick Data 121 Applying Traditional Econometric Techniques to Tick Data 123 Conclusion 125 CHAPTER 10 Trading on Market Microstructure: Inventory Models 127 Overview of Inventory Trading Strategies 129 Orders, Traders, and Liquidity 130 Proﬁtable Market Making 134 Directional Liquidity Provision 139 Conclusion 143 CHAPTER 11 Trading on Market Microstructure: Information Models 145 Measures of Asymmetric Information 146 Information-Based Trading Models 149 Conclusion 164 CHAPTER 12 Event Arbitrage 165 Developing Event Arbitrage Trading Strategies 165 What Constitutes an Event? 167 Forecasting Methodologies 168 Tradable News 173 Application of Event Arbitrage 175 Conclusion 184 CHAPTER 13 Statistical Arbitrage in High-Frequency Settings 185 Mathematical Foundations 186 Practical Applications of Statistical Arbitrage 188 Conclusion 199 viii CONTENTS CHAPTER 14 Creating and Managing Portfolios of High-Frequency Strategies 201 Analytical Foundations of Portfolio Optimization 202 Eﬀective Portfolio Management Practices 211 Conclusion 217 CHAPTER 15 Back-Testing Trading Models 219 Evaluating Point Forecasts 220 Evaluating Directional Forecasts 222 Conclusion 231 CHAPTER 16 Implementing High-Frequency Trading Systems 233 Model Development Life Cycle 234 System Implementation 236 Testing Trading Systems 246 Conclusion 249 CHAPTER 17 Risk Management 251 Determining Risk Management Goals 252 Measuring Risk 253 Managing Risk 266 Conclusion 271 CHAPTER 18 Executing and Monitoring High-Frequency Trading 273 Executing High-Frequency Trading Systems 274 Monitoring High-Frequency Execution 280 Conclusion 281 Contents ix CHAPTER 19 Post-Trade Profitability Analysis 283 Post-Trade Cost Analysis 284 Post-Trade Performance Analysis 295 Conclusion 301 References 303 About the Web Site 323 About the Author 325 Index 327 Acknowledgments This book was made possible by a terrific team at John Wiley & Sons: Deb Englander, Laura Walsh, Bill Falloon, Tiffany Charbonier, Cristin RiffleLash, and Michael Lisk.
Handbook of Modeling High-Frequency Data in Finance by Frederi G. Viens, Maria C. Mariani, Ionut Florescu
algorithmic trading, asset allocation, automated trading system, backtesting, Black-Scholes formula, Brownian motion, business process, continuous integration, corporate governance, discrete time, distributed generation, fixed income, Flash crash, housing crisis, implied volatility, incomplete markets, linear programming, mandelbrot fractal, market friction, market microstructure, martingale, Menlo Park, p-value, pattern recognition, performance metric, principal–agent problem, random walk, risk tolerance, risk/return, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, value at risk, volatility smile, Wiener process
Modeling and forecasting realized volatility. Econometrica 2003;71:579–625. Andersen TG, Bollerslev T, Frederiksen PH, Nielsen MØ. Comment on P. R. Hansen and A. Lunde: realized variance and market microstructure noise. J Bus Econ Stat 2006;24:173–179. Andersen T, Bollerslev T, Meddahi N. Realized volatility forecasting and market microstructure noise. J Econometrics 2010;160:220–234. Bandi FM, Russel JR. Realized covariation, realized beta and microstructure noise. Working Paper, Graduate School of Business, University of Chicago, 2005. Bandi FM, Russel JR. Separating market microstructure noise from volatility. J Financ Econ 2006;79:655–692. Bandi FM, Russell JR. Market microstructure noise, integrated variance estimators, and the accuracy of asymptotic approximations. J Econometrics 2011;160(1):145–159. Bandi FM, Russell JR. Microstructure noise, realized variance and optimal sampling.
., xiv, 347, 383 Market capitalization index, 128 Market completeness assumption, 302 Market complexity, modeling of, 99 Market crash, 346 2008, 136 Market index (indices) exponents calculated for, 345 squared returns of, 220 technique for producing, 110 Market index decrease, spread and, 105 Market inefﬁciencies, for small-space and mid-volume classes, 44 Market microstructure effects, 263 Market microstructure, effects on Fourier estimator, 245 Market microstructure contaminations, 273 Market microstructure model, of ultra high frequency trading, 235–242 Market model, 296–297 Market movement, indicators of, 110 Market reaction, to abnormal price movements, 45 Market-traded option prices, 219 Markov chain, stochastic volatility process with, 401 Markowitz-type optimization, 286 Martingale-difference process, 178. See also Continuous semimartingales; Equivalent martingale measure; Exponential martingale process Supermartingale Matlab, 14, 257 Matlab module, 125, 339 Maximum likelihood estimation (MLE), 13–14, 185 Index ﬁnite-sample performance of, 14–17 performance of, 23–24 Maximum likelihood estimators (MLEs, mles), 4, 6, 172–175, 190, 225.
ACKNOWLEDGMENTS The authors are most grateful for the comments and suggestions received from the anonymous reviewers of this chapter and to other participants at the Conference on Modeling High Frequency Data II at the Stevens Institute of Technology. Support funding for this research is also acknowledged to ICASA (Institute of Complex Additive Systems Analysis), New Mexico Institute of Mining and Technology. 242 CHAPTER 9 A Market Microstructure Model REFERENCES Ait-Shalia Y, Yu J. High frequency market microstructure noise estimates and liquidity measures. Ann Appl Stat 2009;1:422–457. Campbell JY, Lo AW, MacKinlay C. The econometrics of ﬁnancial markets. Princeton, NJ: Princeton University Press; 1997. CFTC-SEC. Findings regarding the market events of May 6, 2010; September 30, 2010. Chamberlin EH. The theory of monopolistic competition. London: Oxford University Press; 1937.
Topics in Market Microstructure by Ilija I. Zovko
Brownian motion, continuous double auction, correlation coefficient, financial intermediation, Gini coefficient, market design, market friction, market microstructure, Murray Gell-Mann, p-value, quantitative trading / quantitative ﬁnance, random walk, stochastic process, stochastic volatility, transaction costs
CONTENTS 3.5 Supplementary Material . . . . . . . . . . . . . . . . 3.5.1 Literature review . . . . . . . . . . . . . . . . 3.5.2 Dimensional analysis . . . . . . . . . . . . . . 3.5.3 The London Stock Exchange (LSE) data set . 3.5.4 Opening auction, real order types, time . . . 3.5.5 Measurement of model parameters . . . . . . 3.5.6 Estimating the errors for the regressions . . . 3.5.7 Market impact . . . . . . . . . . . . . . . . . 3.5.8 Extending the model . . . . . . . . . . . . . . . . . . . . . . . 4 Correlation and clustering in the trading of the members of the LSE 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 The LSE dataset . . . . . . . . . . . . . . . . . 4.1.2 Measuring correlations between strategies . . . 4.2 Significance and structure in the correlation matrices . 4.2.1 Density of the correlation matrix eigenvalue distribution . . . . . . . . . . . . . . . . . . . . 4.2.2 Bootstrapping the largest eigenvalues . . . . . 4.2.3 Clustering of trading behaviour . . . . . . . . . 4.2.4 Time persistence of correlations . . . . . . . . . 4.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 37 37 40 41 43 44 48 52 56 59 59 60 60 64 64 67 68 71 75 5 Market imbalances and stock returns: heterogeneity of order sizes at the LSE 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Distribution of order sizes . . . . . . . . . . . . . . . . 5.3 Order size heterogen. and stock returns . . . . . . . . 5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 77 77 80 82 94 6 Conclusions 97 vi Chapter 1 Introduction The topic of this thesis is Market microstructure. Market microstructure is an area of finance that studies the dynamics and processes through which investors’ forecasts about future asset values are ultimately translated into the assets’ current prices and trading volumes. The field encompasses also the study of trading rules which regulate the markets and constrain the actions of traders. In even broader terms, research directions that deal with the interrelation between institutional structure, strategic behavior, prices and welfare are all considered market microstructure. The topics investigated in this thesis are also related to the field of Econophysics. Econophysics is a multidisciplinary field where ideas from physics and economics meet.
Topics in Market Microstructure Ilija I. Zovko Topics in M a rk et Microstru c t u re ! The publication of this book is in part made possible by the Center for Nonlinear Dynamics in Economics and Finance (CeNDEF) at the University of Amsterdam Lay out: LaTeX, http://www.latex-project.org/ Cover design: René Staelenberg, Amsterdam Cover illustration: Wordle by Jonathan Feinberg, http://wordle.net/ ISBN 978 90 5629 538 7 NUR 780 © I. I. Zovko / Vossiuspers UvA – Amsterdam University Press, 2008 All rights reserved. Without limiting the rights under copyright reserved above, no part of this book may be reproduced, stored in or introduced into a retrieval system, or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording or otherwise) without the written permission of both the copyright owner and the author of the book. !
Without limiting the rights under copyright reserved above, no part of this book may be reproduced, stored in or introduced into a retrieval system, or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording or otherwise) without the written permission of both the copyright owner and the author of the book. ! Topics in Market Microstructure Academisch Proefschrift ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof. dr. D.C. van den Boom ten overstaan van een door het college voor promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op dinsdag 4 november 2008, te 12.00 uur door Ilija I. Zovko geboren te Zagreb, Kroatië Promotiecommissie: Promotor: Prof. Dr. C.H. Hommes Co-Promotor: Prof. Dr. J.D. Farmer Overige laden: Prof. Dr. H.P. Boswijk Prof. Dr. C.G.H. Diks Prof. Dr. F.C.J.M. de Jong Prof. Dr. T. Lux Faculteit Economie en Bedrijfskunde Universiteit van Amsterdam Acknowledgements The research in this thesis has been made possible by the contribution of numerous people with whom I have had the privilege to work with and learn from.
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 process, buy low sell high, capital asset pricing model, centre right, collateralized debt obligation, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, discounted cash flows, disintermediation, diversification, 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, Nick Leeson, P = NP, pattern recognition, 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 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
Being the newbie that I was, I had no idea that Evan was “the father of program trading.”9 He had done the first package trade at Keystone and later moved to Batterymarch, where those early trades involved running across town with decks of punched cards. The athletic aspect to Evan’s electronic trading continued long past the time it was needed for data communications. Few others have been observed doing cartwheels in trading rooms. In between gymnastic events, Evan taught me a great deal about market microstructure, and the incentives of the various participants in the markets. His pioneering work in creating electronic markets, by direct computer links to brokers before the exchanges had moved beyond telephones, presaged much of the complexity of current network of electronic markets, while illuminating the critical relationships and incentives. He was the first person to have an electronic order front-run by a broker.
I believe that while there is a lot of efficiency in markets, there is no god determining fair prices. Prices are determined by agents acting in their singular self-interest. The only way anyone knows a fair, equilibrium price has been established is for prices to overreact in both directions. I have set out to profit by making this process more efficient. In the two areas I really know anything about, options pricing and market microstructure, I read virtually every book and article published on those subjects and then find things to exploit that are not written about. Lastly, no model or mathematical insight can make money on its own. One needs a team that collectively understands every aspect of running a trading business. Becoming a quant is not an individual sport. JWPR007-Lindsey April 30, 2007 16:14 328 JWPR007-Lindsey April 30, 2007 16:23 Chapter 24 John F. ( Jack) Marshall Senior Principal of Marshall, Tucker & Associates, LLC, and Vice Chairman of the International Securities Exchange W ithin the financial engineering community, I am perhaps best known to many as the person who gave definition to the field.
Sterge developed options pricing models that captured the effects of fat-tailed and skewed distributions, as well as investors’ relative risk aversion for the downside versus upside insurance aspect of options. Mr. Sterge was promoted to partner in 1993. Prior to joining Cooper Neff, Mr. Sterge was employed by CoreStates Financial Corporation where he was assistant vice president, trading interest rate options from September 1986 to November 1989. In 1991, Mr. Sterge founded a new variety of short term equity trading based on models of stock market microstructure, or how stocks’ bids and offers evolve over time and in response to order flow and other information. Called Active Portfolio Strategies, this business flourished following the acquisition of Cooper Neff by Banque Nationale de Paris (now BNP Paribas) in 1995. Effectively, an internal hedge fund strategy, Active Portfolio Strategies at times managed well over $20 billion in global equity positions for BNP Paribas.
Analysis of Financial Time Series by Ruey S. Tsay
Asian financial crisis, asset allocation, Black-Scholes formula, Brownian motion, 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
M. (1994), “Threshold heteroscedastic models,” Journal of Economic Dynamics and Control, 18, 931–955. Analysis of Financial Time Series. Ruey S. Tsay Copyright 2002 John Wiley & Sons, Inc. ISBN: 0-471-41544-8 CHAPTER 5 High-Frequency Data Analysis and Market Microstructure High-frequency data are observations taken at fine time intervals. In finance, they often mean observations taken daily or at a finer time scale. These data have become available primarily due to advances in data acquisition and processing techniques, and they have attracted much attention because they are important in empirical study of market microstructure. The ultimate high-frequency data in finance are the transaction-by-transaction or trade-by-trade data in security markets. Here time is often measured in seconds. The Trades and Quotes (TAQ) database of the New York Stock Exchange (NYSE) contains all equity transactions reported on the Consolidated Tape from 1992 to present, which includes transactions on NYSE, AMEX, NASDAQ, and the regional exchanges.
Conditional Heteroscedastic Models 3.1 3.2 3.3 3.4 3.5 3.6 3.7 79 Characteristics of Volatility, 80 Structure of a Model, 81 The ARCH Model, 82 The GARCH Model, 93 The Integrated GARCH Model, 100 The GARCH-M Model, 101 The Exponential GARCH Model, 102 vii viii CONTENTS 3.8 The CHARMA Model, 107 3.9 Random Coefficient Autoregressive Models, 109 3.10 The Stochastic Volatility Model, 110 3.11 The Long-Memory Stochastic Volatility Model, 110 3.12 An Alternative Approach, 112 3.13 Application, 114 3.14 Kurtosis of GARCH Models, 118 Appendix A. Some RATS Programs for Estimating Volatility Models, 120 4. Nonlinear Models and Their Applications 126 4.1 Nonlinear Models, 128 4.2 Nonlinearity Tests, 152 4.3 Modeling, 161 4.4 Forecasting, 161 4.5 Application, 164 Appendix A. Some RATS Programs for Nonlinear Volatility Models, 168 Appendix B. S-Plus Commands for Neural Network, 169 5. High-Frequency Data Analysis and Market Microstructure 175 5.1 Nonsynchronous Trading, 176 5.2 Bid-Ask Spread, 179 5.3 Empirical Characteristics of Transactions Data, 181 5.4 Models for Price Changes, 187 5.5 Duration Models, 194 5.6 Nonlinear Duration Models, 206 5.7 Bivariate Models for Price Change and Duration, 207 Appendix A. Review of Some Probability Distributions, 212 Appendix B. Hazard Function, 215 Appendix C. Some RATS Programs for Duration Models, 216 6.
In Chapter 4, we address nonlinearity in financial time series, introduce test statistics that can discriminate nonlinear series from linear ones, and discuss several nonlinear models. The chapter also introduces nonparametric estimation methods and neural networks and shows various applications of nonlinear models in finance. Chapter 5 is concerned with analysis of high-frequency financial data and its application to market microstructure. It shows that nonsynchronous trading and bid-ask bounce can introduce serial correlations in a stock return. It also studies the dynamic of time duration between trades and some econometric models for analyzing transactions data. In Chapter 6, we introduce continuous-time diffusion models and Ito’s lemma. BlackScholes option pricing formulas are derived and a simple jump diffusion model is used to capture some characteristics commonly observed in options markets.
Trend Following: How Great Traders Make Millions in Up or Down Markets by Michael W. Covel
Albert Einstein, asset allocation, Atul Gawande, backtesting, Bernie Madoff, Black Swan, buy low sell high, capital asset pricing model, Clayton Christensen, commodity trading advisor, correlation coefficient, Daniel Kahneman / Amos Tversky, delayed gratification, deliberate practice, diversification, diversified portfolio, Elliott wave, Emanuel Derman, Eugene Fama: efficient market hypothesis, fiat currency, fixed income, game design, hindsight bias, housing crisis, index fund, Isaac Newton, John Nash: game theory, linear programming, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, mental accounting, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Renaissance Technologies, Richard Feynman, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, systematic trading, the scientific method, Thomas L Friedman, too big to fail, transaction costs, upwardly mobile, value at risk, Vanguard fund, volatility arbitrage, William of Occam
Irvington-on-Hudson, NY: The Foundation for Economic Education, Inc., 1996, printed 1998. First published 1949. 7. Larry Harris, Trading and Exchanges: Market Microstructure for Practitioners. New York: Oxford University Press, 2003. 8. David Greising, How Managed Funds Managed to Do So Poorly. Business Week, No. 3294 (November 23, 1992), 112. 9. Daniel P. Collins, The Return of Long-Term Trend Following. Futures, Vol. 32, No. 4 (March 2003), 68–73. 10. Desmond McRae, Top Traders. Managed Derivatives (May 1996). 11. Trend Following: Performance, Risk and Correlation Characteristics. White Paper, Graham Capital Management. 12. Larry Harris, Trading and Exchanges: Market Microstructure for Practitioners. New York: Oxford University Press, 2003. 13. Ben Warwick, The Holy Grail of Managed Futures (cover story). Managed Account Reports (MAR), No. 267 (May 2001), 1. 14.
Henry & Company, Inc. 407 408 Trend Following (Updated Edition): Learn to Make Millions in Up or Down Markets 28. Ginger Szala, Tom Shanks: Former “Turtle” Winning Race the Hard Way. Futures, Vol. 20, No. 2 (January 15, 1991), 78. 29. Carla Cavaletti, Turtles on the Move. Futures, Vol. 27 (June 1998), 79. 30. Laurie Kaplan, Turning Turtles into Traders. Managed Derivatives (May 1996). 31. Larry Harris, Trading and Exchanges: Market Microstructure for Practioners. New York: Oxford University Press, 2003. 32. Larry Harris, The Winners and Losers of the Zero-Sum Game: The Origins of Trading Profits, Price Efficiency and Market Liquidity. Draft 0.911, May 7, 1993. 33. Larry Harris, The Winners and Losers of the Zero-Sum Game: The Origins of Trading Profits, Price Efficiency and Market Liquidity. Draft 0.911, May 7, 1993. 34. Larry Harris, The Winners and Losers of the Zero-Sum Game: The Origins of Trading Profits, Price Efficiency and Market Liquidity.
Commencement address given before the graduating class of 1989, University of Georgia, June 17, 1989. 5. Gibbons Burke, Managing Your Money. Active Trader (July 2000). 6. Mark Rzepczynski, Portfolio Diversification: Investors Just Don’t Seem to Have Enough. JWH Journal. 7. Jack Reerink, The Power of Leverage. Futures, Vol. 24, No. 4 (April 1995). 8. Edward O. Thorp, The Mathematics of Gambling. Hollywood, CA, 1984. 9. Larry Harris, Trading and Exchanges: Market Microstructure for Practitioners. New York: Oxford University Press, 2003. 10. Going Once, Going Twice. Discover (August 2002), 23. 11. Jim Little, Sol Waksman, A Perspective on Risk. Barclay Managed Futures Report. 12. Craig Pauley, How to Become a CTA. Based on Chicago Mercantile Exchange Seminars, 1992–1994. June 1994. 13. Thomas L. Friedman, The Lexus and The Olive Tree. New York: Farrar, Straus, Giroux, 1999. 14.
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, 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, transaction costs, value at risk, volatility smile, Wiener process, yield curve
More generally, a considerable body of financial econometrics research has focused on discrete time models for the theory of asset pricing which depends on the assumptions of no arbitrage, single agent optimality and market equilibrium. Indeed, two out of five of the volumes of classic research papers in financial econometrics collected by Lo (2007) are devoted to this issue. I.4.6.2 Analysing Empirical Market Behaviour Market microstructure is the study of price formation and how this is related to trading protocols and trading volume. It is the study of market liquidity, of how prices are formed, of the times between trades and of the determinants of the bid–ask spread. A good survey of research papers in this field is given by Biais et al. (2005). High quality tic-by-tic data on every trade or quote that is made may be analysed for patterns.
Berben, R.P. and Jansen, W.J. (2005) Comovement in international equity markets: A sectoral view. Journal of International Money and Finance 24, 832–857. Bernoulli, D. (1738) Specimen theoria novae de mensura sortis. Commentarii Academiae Scientarum Imperialis Petropolitnae 5(2), 175–192. Translated into English by L. Sommer (1954): Exposition of a new theory on the measurement of risk, Econometrica, 22, 23–26. Biais, B.R., Glosten, L. and Spatt, C. (2005) Market microstructure: A survey of micro-foundations, empirical results, and policy implications. Journal of Financial Markets 8, 217–264. Bilmes, J.A. (1998) A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. http://crow.ee.washington.edu/people/bulyko/papers/ em.pdf (accessed October 2007). Black, F. and Scholes, M. (1973) The pricing of options and corporate liabilities.
(independent and identically distributed) variables central limit theorem 121 error process 148 financial modelling 186 GEV distribution 101 regression 148, 157, 175 stable distribution 106 stochastic process 134–5 Implicit function 185 Implied volatility 194, 196, 200–1 Implied volatility surface 200–1 Incremental change 31 Indefinite integral 15 Independent events 74 Independent and identically distributed (i.i.d.) variables central limit theorem 121 error process 148 financial modelling 186 GEV distribution 101 regression 148, 157, 175 stable distribution 106 stochastic process 134–5 284 Index Independent variable 72, 143 random 109–10, 115, 140 Index tracking regression model 182–3 Indicator function 6 Indices, laws 8 Indifference curves 248–9 Inequality constraint, minimum variance portfolio 245–6 Inference 72, 118–29, 141 central limit theorem 120–1 confidence intervals 72, 118–24 critical values 118–20, 122–3, 129 hypothesis tests 124–5 means 125–7 non-parametric tests 127–9 quantiles 118–20 variance 126–7 Inflexion points 14, 35 Information matrix 133, 203 Information ratio 257, 259 Instability, finite difference approximation 209–10 Integrated process, discrete time 134–6 Integration 3, 15–16, 35 Intensity, Poisson distribution 88 Interest rate 34, 171–3 Interest rate sensitivity 34 Interpolation 186, 193–200, 223 cubic spline 197–200 currency option 195–7 linear/bilinear 193–5 polynomial 195–7 Intrinsic value of option 215 Inverse function 6–7, 35 Inverse matrix 41, 43–4, 133 Investment bank 225 Investment 2, 256–7 Investor risk tolerance 230–1, 237 Irrational numbers 7 Isoquants 248 Iteration 186–93, 223 bisection method 187–8 gradient method 191–3 Newton–Raphson method 188–91 Itô’s lemma 138–9, 219 iTraxx Europe credit spread index 172 Jacobian matrix 202 Jarque–Bera normality test Jensen’s alpha 257–8 158 Joint density function 114–15 Joint distribution function 114–15 Joint probability 73 Jumps, Poisson process 139 Kappa indices 263–5 Kernel 106–7 Kolmogorov–Smirnoff test 128 Kuhn–Tucker conditions 30 Kurtosis 81–3, 94–6, 205–6 Lagrange multiplier (LM) test 124, 167 Lagrange multiplier 29–30, 244 Lagrangian function 29–30 Lattice 186, 210–16, 223 Laws of indices 8 Least squares OLS estimation 143–4, 146–50, 153–61, 163, 170–1, 176 problems 201–2 weighted 179 Leptokurtic density 82–3 Levenberg–Marquardt algorithm 202 Lévy distribution 105 Likelihood function 72, 130–31 MLE 72, 130–34, 141, 202–3 optimization 202–3 ratio test 124, 167 Linear function 4–5 Linear interpolation 193–5 Linear portfolios 33, 35 correlation matrix 55–60 covariance matrix 55–61 matrix algebra 55–61 P&L 57–8 returns 25, 56–8 volatility 57–8 Linear regression 143–84 Linear restrictions, hypothesis tests 165–6 Linear transformation 48 Linear utility function 233 LM (Lagrange multiplier) 29–30, 124, 167, 244 Local maxima 14, 28–9 Local minima 14, 28–9 Logarithmic utility function 232 Logarithm, natural 1, 9, 34–5 Log likelihood 131–2 Lognormal distribution 93–4, 213–14, 218–20 Log returns 16, 19–25 Index Long portfolio 3, 17, 238–40 Long-short portfolio 17, 20–1 Low discrepancy sequences 217 Lower triangular square matrix 62, 64 LR (likelihood ratio) test 124, 167 LU decomposition, matrix 63–4 Marginal densities 108–9 Marginal distributions 108–9 Marginal probability 73–4 Marginal utility 229–30 Market behaviour 180–1 Market beta 250 Market equilibrium 252 Market maker 2 Market microstructure 180 Market portfolio 250–1 Market risk premium, CAPM 253 Markets complete 212 regime-specific behaviour 96–7 Markowitz, Harry 226, 238, 266 Markowitz problem 200–1, 226, 244–5 Matrix algebra 37–70 application 38–47 decomposition 61–4, 70 definite matrix 37, 46–7, 54, 58–9, 70 determinant 41–3, 47 eigenvalues/vectors 37–8, 48–54, 59–61, 70 functions of several variables 27–31 general linear model 161–2 hypothesis testing 165–6 invariant 62 inverse 41, 43–4 law 39–40 linear portfolio 55–61 OLS estimation 159–61 PCA 64–70 product 39–40 quadratic form 37, 45–6, 54 regression 159–61, 165–6 simultaneous equation 44–5 singular matrix 40–1 terminology 38–9 Maxima 14, 28–31, 35 Maximum likelihood estimation (MLE) 72, 130–4, 141, 202–3 Mean confidence interval 123 Mean excess loss 104 Mean reverting process 136–7 Mean 78–9, 125–6, 127, 133–4 285 Mean square error 201 Mean–variance analysis 238 Mean–variance criterion, utility theory 234–7 Minima 14, 28–31, 35 Minimum variance portfolio 3, 240–7 Mixture distribution 94–7, 116–17, 203–6 MLE (maximum likelihood estimation) 72, 130–4, 141, 202–3 Modified duration 2 Modified Newton method 192–3 Moments probability distribution 78–83, 140 sample 82–3 Sharpe ratio 260–3 Monotonic function 13–14, 35 Monte Carlo simulation 129, 217–22 correlated simulation 220–2 empirical distribution 217–18 random numbers 217 time series of asset prices 218–20 Multicollinearity 170–3, 184 Multiple restrictions, hypothesis testing 166–7 Multivariate distributions 107–18, 140–1 bivariate 108–9, 116–17 bivariate normal mixture 116–17 continuous 114 correlation 111–14 covariance 110–2 independent random variables 109–10, 114 normal 115–17, 220–2 Student t 117–18 Multivariate linear regression 158–75 BHP Billiton Ltd 162–5, 169–70, 174–5 confidence interval 167–70 general linear model 161–2 hypothesis testing 163–6 matrix notation 159–61 multicollinearity 170–3, 184 multiple regression in Excel 163–4 OLS estimation 159–61 orthogonal regression 173–5 prediction 169–70 simple linear model 159–61 Multivariate Taylor expansion 34 Mutually exclusive events 73 Natural logarithm 9, 34–5 Natural spline 198 Negative definite matrix 46–7, 54 Newey–West standard error 176 286 Index Newton–Raphson iteration 188–91 Newton’s method 192 No arbitrage 2, 179–80, 211–12 Non-linear function 1–2 Non-linear hypothesis 167 Non-linear portfolio 33, 35 Non-parametric test 127–9 Normal confidence interval 119–20 Normal distribution 90–2 Jarque–Bera test 158 log likelihood 131–2 mixtures 94–7, 140–1, 203–6 multivariate 115–16, 220–2 standard 218–19 Normalized eigenvector 51–3 Normalized Student t distribution 99 Normal mixture distribution 94–7, 116–17, 140–1 EM algorithm 203–6 kurtosis 95–6 probabilities of variable 96–7 variance 94–6 Null hypothesis 124 Numerical methods 185–223 binomial lattice 210–6 inter/extrapolation 193–200 iteration 186–93 Objective function 29, 188 Offer price 2 Oil index, Amex 162–3, 169–70, 174 OLS (ordinary least squares) estimation 143–4, 146–50 autocorrelation 176 BHP Billiton Ltd case study 163 heteroscedasticity 176 matrix notation 159–61 multicollinearity 170–1 properties of estimator 155–8 regression in Excel 153–5 Omega statistic 263–5 One-sided confidence interval 119–20 Opportunity set 246–7, 251 Optimization 29–31, 200–6, 223 EM algorithm 203–6 least squares problems 201–2 likelihood methods 202–3 numerical methods 200–5 portfolio allocation 3, 181 Options 1–2 American 1, 215–16 Bermudan 1 call 1, 6 currency 195–7 European 1–2, 195–6, 212–13, 215–16 finite difference approximation 206–10 pay-off 6 plain vanilla 2 put 1 Ordinary least squares (OLS) estimation 143–4, 146–50 autocorrelation 176 BHP Billiton Ltd case study 163 heteroscedasticity 176 matrix notation 159–61 multicollinearity 170–1 properties of estimators 155–8 regression in Excel 153–5 Orthogonal matrix 53–4 Orthogonal regression 173–5 Orthogonal vector 39 Orthonormal matrix 53 Orthonormal vector 53 Out-of-sample testing 183 P&L (profit and loss) 3, 19 backtesting 183 continuous time 19 discrete time 19 financial returns 16, 19 volatility 57–8 Pairs trading 183 Parabola 4 Parameter notation 79–80 Pareto distribution 101, 103–5 Parsimonious regression model 153 Partial derivative 27–8, 35 Partial differential equation 2, 208–10 Pay-off, option 6 PCA (principal component analysis) 38, 64–70 definition 65–6 European equity indices 67–9 multicollinearity 171 representation 66–7 Peaks-over-threshold model 103–4 Percentage returns 16, 19–20, 58 Percentile 83–5, 195 Performance measures, RAPMs 256–65 Period log returns 23–5 Pi 7 Index Piecewise polynomial interpolation 197 Plain vanilla option 2 Points of inflexion 14, 35 Poisson distribution 87–9 Poisson process 88, 139 Polynomial interpolation 195–7 Population mean 123 Portfolio allocation 237–49, 266 diversification 238–40 efficient frontier 246–9, 251 Markowitz problem 244–5 minimum variance portfolio 240–7 optimal allocation 3, 181, 247–9 Portfolio holdings 17–18, 25–6 Portfolio mathematics 225–67 asset pricing theory 250–55 portfolio allocation 237–49, 266 RAPMs 256–67 utility theory 226–37, 266 Portfolios bond portfolio 37 delta-hedged 208 linear 25, 33, 35, 55–61 minimum variance 3, 240–7 non-linear 33, 35 rebalancing 17–18, 26, 248–9 returns 17–18, 20–1, 91–2 risk factors 33 risk free 211–12 stock portfolio 37 Portfolio volatility 3 Portfolio weights 3, 17, 25–6 Positive definite matrices 37, 46–7, 70 correlation matrix 58–9 covariance matrix 58–9 eigenvalues/vectors 54 stationary point 28–9 Posterior probability 74 Post-sample prediction 183 Power series expansion 9 Power utility functions 232–3 Prediction 169–70, 183 Price discovery 180 Prices ask price 2 asset price evolution 87 bid price 2 equity 172 generating time series 218–20 lognormal asset prices 213–14 market microstructure 180 offer price 2 stochastic process 137–9 Pricing arbitrage pricing theory 257 asset pricing theory 179–80, 250–55 European option 212–13 no arbitrage 211–13 Principal cofactors, determinants 41 Principal component analysis (PCA) 38, 64–70 definition 65–6 European equity index 67–9 multicollinearity 171 representation 66–7 Principal minors, determinants 41 Principle of portfolio diversification 240 Prior probability 74 Probability and statistics 71–141 basic concepts 72–85 inference 118–29 laws of probability 73–5 MLE 130–4 multivariate distributions 107–18 stochastic processes 134–9 univariate distribution 85–107 Profit and loss (P&L) 3, 19 backtesting 183 continuous time 19 discrete time 19 financial returns 16, 19 volatility 57–8 Prompt futures 194 Pseudo-random numbers 217 Put option 1, 212–13, 215–16 Quadratic convergence 188–9, 192 Quadratic form 37, 45–6, 54 Quadratic function 4–5, 233 Quantiles 83–5, 118–20, 195 Quartiles 83–5 Quasi-random numbers 217 Random numbers 89, 217 Random variables 71 density/distribution function 75 i.i.d. 101, 106, 121, 135, 148, 157, 175 independent 109–10, 116, 140–1 OLS estimators 155 sampling 79–80 Random walks 134–7 Ranking investments 256 287 288 Index RAPMs (risk adjusted performance measures) 256–67 CAPM 257–8 kappa indices 263–5 omega statistic 263–5 Sharpe ratio 250–1, 252, 257–63, 267 Sortino ratio 263–5 Realization, random variable 75 Realized variance 182 Rebalancing of portfolio 17–18, 26, 248–9 Recombining tree 210 Regime-specific market behaviour 96–7, 117 Regression 143–84 autocorrelation 175–9, 184 financial applications 179–83 heteroscedasticity 175–9, 184 linear 143–84 multivariate linear 158–75 OLS estimator properties 155–8 simple linear model 144–55 Relative frequency 77–8 Relative risk tolerance 231 Representation, PCA 66–7 Residuals 145–6, 157, 175–8 Residual sum of squares (RSS) 146, 148–50, 159–62 Resolution techniques 185–6 Restrictions, hypothesis testing 165–7 Returns 2–3, 16–26 absolute 58 active 92, 256 CAPM 253–4 compounding 22–3 continuous time 16–17 correlated simulations 220 discrete time 16–17, 22–5 equity index 96–7 geometric Brownian motion 21–2 linear portfolio 25, 56–8 log returns 16, 19–25 long-short portfolio 20–1 multivariate normal distribution 115–16 normal probability 91–2 P&L 19 percentage 16, 19–20, 59–61 period log 23–5 portfolio holdings/weights 17–18 risk free 2 sources 25–6 stochastic process 137–9 Ridge estimator, OLS 171 Risk active risk 256 diversifiable risk 181 portfolio 56–7 systematic risk 181, 250, 252 Risk adjusted performance measure (RAPM) 256–67 CAPM 257–8, 266 kappa indices 263–5 omega statistic 263–5 Sharpe ratio 251, 252, 257–63, 267 Sortino ratio 263–5 Risk averse investor 248 Risk aversion coefficients 231–4, 237 Risk factor sensitivities 33 Risk free investment 2 Risk free portfolio 211 Risk free returns 2 Risk loving investors 248–9 Risk neutral valuation 211–12 Risk preference 229–30 Risk reversal 195–7 Risk tolerance 230–1, 237 Robustness 171 Roots 3–9, 187 RSS (residual sum of squares) 146, 148–50, 159–62 S&P 100 index 242–4 S&P 500 index 204–5 Saddle point 14, 28 Sample 76–8, 82–3 Sampling distribution 140 Sampling random variable 79–80 Scalar product 39 Scaling law 106 Scatter plot 112–13, 144–5 SDE (stochastic differential equation) 136 Security market line (SML) 253–4 Self-financing portfolio 18 Sensitivities 1–2, 33–4 Sharpe ratio 257–63, 267 autocorrelation adjusted 259–62 CML 251, 252 generalized 262–3 higher moment adjusted 260–2 making decision 258 stochastic dominance 258–9 Sharpe, William 250 Short portfolio 3, 17 22, 134, Index Short sales 245–7 Short-term hedging 182 Significance level 124 Similarity transform 62 Similar matrices 62 Simple linear regression 144–55 ANOVA and goodness of fit 149–50 error process 148–9 Excel OLS estimation 153–5 hypothesis tests 151–2 matrix notation 159–61 OLS estimation 146–50 reporting estimated model 152–3 Simulation 186, 217–22 Simultaneous equations 44–5 Singular matrix 40–1 Skewness 81–3, 205–6 Smile fitting 196–7 SML (security market line) 253–4 Solver, Excel 186, 190–1, 246 Sortino ratio 263–5 Spectral decomposition 60–1, 70 Spline interpolation 197–200 Square matrix 38, 40–2, 61–4 Square-root-of-time scaling rule 106 Stable distribution 105–6 Standard deviation 80, 121 Standard error 80, 169 central limit theorem 121 mean/variance 133–4 regression 148–9 White’s robust 176 Standard error of the prediction 169 Standardized Student t distribution 99–100 Standard normal distribution 90, 218–19 Standard normal transformation 90 Standard uniform distribution 89 Stationary point 14–15, 28–31, 35 Stationary stochastic process 111–12, 134–6 Stationary time series 64–5 Statistical arbitrage strategy 182–3 Statistical bootstrap 218 Statistics and probability 71–141 basic concepts 72–85 inference 118–29 law of probability 73–5 MLE 130–4 multivariate distribution 107–18 stochastic process 134–9 univariate distribution 85–107 Step length 192 Stochastic differential equation (SDE) 22, 134, 136 Stochastic dominance 227, 258–9 Stochastic process 72, 134–9, 141 asset price/returns 137–9 integrated 134–6 mean reverting 136–7 Poisson process 139 random walks 136–7 stationary 111–12, 134–6 Stock portfolio 37 Straddle 195–6 Strangle 195–7 Strictly monotonic function 13–14, 35 Strict stochastic dominance 258 Structural break 175 Student t distribution 97–100, 140 confidence intervals 122–3 critical values 122–3 equality of means/variances 127 MLE 132 multivariate 117–18 regression 151–3, 165, 167–8 simulation 220–2 Sum of squared residual, OLS 146 Symmetric matrix 38, 47, 52–4, 61 Systematic risk 181, 250, 252 Tail index 102, 104 Taylor expansion 2–3, 31–4, 36 applications 33–4 approximation 31–4, 36 definition 32–3 multivariate 34 risk factor sensitivities 33 Theory of asset pricing 179–80, 250–55 Tic-by-tic data 180 Time series asset prices/returns 137–9, 218–20 lognormal asset prices 218–20 PCA 64–5 Poisson process 88 regression 144 stochastic process 134–9 Tobin’s separation theorem 250 Tolerance levels, iteration 188 Tolerance of risk 230–1, 237 Total derivative 31 Total sum of square (TSS) 149, 159–62 289 290 Index Total variation, PCA 66 Tower law for expectations 79 Traces of matrix 62 Tradable asset 1 Trading, regression model 182–3 Transition probability 211–13 Transitive preferences 226 Transposes of matrix 38 Trees 186, 209–11 Treynor ratio 257, 259 TSS (total sum of squares) 149, 159–62 Two-sided confidence interval 119–21 Unbiased estimation 79, 81, 156–7 Uncertainty 71 Unconstrained optimization 29 Undiversifiable risk 252 Uniform distribution 89 Unit matrix 40–1 Unit vector 46 Univariate distribution 85–107, 140 binomial 85–7, 212–13 exponential 87–9 generalized Pareto 101, 103–5 GEV 101–3 kernel 106–7 lognormal 93–4, 213–14, 218–20 normal 90–7, 115–16, 131–2, 140, 157–8, 203–6, 217–22 normal mixture 94–7, 140, 203–6 Poisson 87–9 sampling 100–1 stable 105–6 Student t 97–100, 122–3, 126, 132–3, 140–1, 151–3, 165–8, 220–2 uniform 89 Upper triangular square matrix 62, 64 Utility theory 226–37, 266 mean–variance criterion 234–7 properties 226–9 risk aversion coefficient 231–4, 237 risk preference 229–30 risk tolerance 230–1, 237 Value at risk (VaR) 104–6, 185, 194 Vanna–volga interpolation method 196 Variance ANOVA 143–4, 149–50, 154, 159–60, 164–5 confidence interval 123–4 forecasting 182 minimum variance portfolio 3, 240–7 mixture distribution 94–6 MLE 133 normal mixture distribution 95–6 portfolio volatility 3 probability distribution 79–81 realized 182 tests on variance 126–7 utility theory 234–7 VaR (value at risk) 104–6, 185, 194 Vector notation, functions of several variables 28 Vectors 28, 37–9, 48–54, 59–61, 70 Venn diagram 74–5 Volatility equity 3, 172–3 implied volatility 194, 196–7, 200–1 interpolation 194, 196–7 linear portfolio 57–8 long-only portfolio 238–40 minimum variance portfolio 240–4 portfolio variance 3 Volpi, Leonardo 70 Vstoxx index 172 Waiting time, Poisson process 88–9 Wald test 124, 167 Weakly stationary process 135 Weak stochastic dominance 258–9 Weibull distribution 103 Weighted least squares 179 Weights, portfolio 3, 17, 25–6 White’s heteroscedasticity test 177–8 White’s robust standard errors 176 Wiener process 22, 136 Yield 1, 197–200 Zero matrix 39 Z test 126
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, 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
In practice, even for very liquid instruments, if we want to keep constant sub-intervals of time during a trading session, it becomes dangerous to go below 5-minute time intervals, to avoid facing empty or nearly empty sub-intervals (a way to escape this problem is by considering non-constant time intervals). For a time sub-interval of width h, a realized volatility can be modeled by starting from the following relationship, assuming a continuous sample path over h: Clearly, going further would exceed the framework of this book.10 Entering into such time sub-intervals is relevant to the broader field of market microstructure, which studies how successive market prices (called tick data) are actually affected by the successive trading orders. Market microstructure represents one of the most ambitious and sophisticated research areas in the field of modeling of the financial markets. 12.4 MODELING THE CORRELATION It makes sense to also deal in this chapter with correlation modeling. Indeed, correlation is linked to the volatilities σ1 & σ2 of two different time series, 1 and 2, through the basic relationship in statistics (for data series 1 & 2) involving the covariance σ1, 2 as well.
To run the NN, we determine the coefficients of the model (the regression parameters) from a subset of the data, in a “learning” phase; then the model is applied to another subset of the data, to check its validity. Figure 15.5 Diagram of a neural network performing a multi-linear regression The major problem with the application of NNs to forecast financial time series is that, as has already appeared in previous sections, financial time series are all but stable in their behavior over time. Hence the revival of this technique, aiming at applying it short term as a tool for market microstructure analysis.7 15.2 POTENTIAL TROUBLES WITH DERIVATIVES VALUATION It's puzzling why bankers have come up with these new ways to lose money when the old ways were working so well. John STUMPF, CEO Wells Fargo8 Throughout this book, we have presented the main quantitative methods to value financial instruments, and outlined some more sophisticated ones, that represent the unceasing research to improve them.
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, butterfly effect, buttonwood tree, buy low sell high, capital asset pricing model, citizen journalism, collateralized debt obligation, corporate governance, Craig Reynolds: boids flock, 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, Gordon Gekko, implied volatility, index arbitrage, index fund, information retrieval, Internet Archive, John Nash: game theory, Khan Academy, 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, moral hazard, mutually assured destruction, natural language processing, 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, Richard Stallman, risk tolerance, risk-adjusted returns, risk/return, Ronald Reagan, 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
Being the newbie that I was, I had no idea that Evan was “the father of program trading.”14 He had done the first package trade, at Keystone, and later moved to Batterymarch, where those early trades involved running across town with decks of punched cards. The athletic aspect to Evan’s electronic trading continued long past the time it was needed for data communications. Few others have been observed doing cartwheels in trading rooms. In between gymnastic events, Evan taught me a great deal about market microstructure, and the incentives of the various participants in the markets. His pioneering work in creating electronic markets, by direct computer links to brokers before the exchanges had moved beyond telephones, presaged much of the complexity of the current network of electronic markets, while illuminating the critical relationships and incentives. He was the first person to have an electronic order front-run by a broker—not that such a thing could happen today.15 A couple of paragraphs are really inadequate to convey Evan’s insights.
Ticker tape, like the roof, hand signals, and the telegraph, was a huge success, probably the most important technology in finance up to that time. People set up jumbo magnifying lens devices to project them onto walls. Back then, people traded faster than the machines could keep up with, so delay meters were installed on the floor. Delay indicators are still found on modern electronic feeds. People saved tapes and studied them. You could say they were the first high-frequency market microstructure studies. Here’s a fellow doing just that. This looks like my office, but neater—a foreshock of the information explosion we have today. 20 Nerds on Wall Str eet On the floor, there were “human Quotrons” who used to pick up the most recent end of tape and follow it back in time to find the latest price quotes for specific stocks. This wasn’t that long ago. Frank Baxter, former chairman at Jefferies and a recent U.S. ambassador to Uruguay, started out doing this.
Shaw & Company in 1988 with $28 million (it now has current assets exceeding $30 billion).7 What is likely Shaw’s last publication on trading dealt with the mechanics of interfacing Unix systems with the Gr eatest Hits of Computation in Finance 41 current generation of electronic trading systems. He apparently realized that, despite his instincts as a former academic, some things are more valuable unpublished. Subsequent in-house developments made D.E. Shaw a leader (reportedly) in electronic market making, statistical arbitrage, and other fast electronic trading strategies. David Whitcomb, a market microstructure economist at Rutgers University and coauthor of a 1988 book on electronic trading strategies,8 faced the same sort of skepticism selling his ideas to Wall Street. Finding no institutional backing, he joined forces with a computer scientist colleague to found Automated Trading Desk (ATD) in the proverbial garage in Charleston, South Carolina. The firm reportedly grew from its first trade in 1990 to one of the leading electronic market participants, trading on average more than 200 million shares daily, or 6 percent of the volume on both the New York Stock Exchange and NASDAQ.9 Figure 2.6 shows the core idea behind ATD’s early trading systems.
Andrei Shleifer, asset allocation, capital asset pricing model, correlation coefficient, cross-subsidies, Daniel Kahneman / Amos Tversky, diversified portfolio, endowment effect, index arbitrage, index fund, locking in a profit, Long Term Capital Management, loss aversion, margin call, market friction, market microstructure, mental accounting, merger arbitrage, new economy, prediction markets, price stability, profit motive, random walk, Richard Thaler, risk-adjusted returns, risk/return, Sharpe ratio, short selling, transaction costs, Vanguard fund
Lease, Kose John, Avner Kalay, Uri Loewenstein, and Oded H. Sarig Value Based Management: The Corporate Response to Shareholder Revolution John D. Martin and J. William Petty Debt Management: A Practitioner’s Guide John D. Finnerty and Douglas R. Emery Real Estate Investment Trusts: Structure, Performance, and Investment Opportunities Su Han Chan, John Erickson, and Ko Wang Trading and Exchanges: Market Microstructure for Practitioners Larry Harris BEYOND THE RANDOM WALK A Guide to Stock Market Anomalies and Low-Risk Investing VIJAY SINGAL PH.D., CFA 2003 Oxford New York Auckland Bangkok Buenos Aires Cape Town Chennai Dar es Salaam Delhi Hong Kong Istanbul Karachi Kolkata Kuala Lumpur Madrid Melbourne Mexico City Mumbai Nairobi São Paulo Shanghai Taipei Tokyo Toronto Copyright © 2003 by Oxford University Press, Inc.
Large Price Declines, News, Liquidity, and Trading Strategies: An Intraday Analysis. Working paper, Department of Finance, University of South Carolina. Larsen, Stephen J., and Jeff Madura. 2003. What Drives Stock Price Behavior Following Extreme One-Day Returns. Journal of Financial Research 26(1), 129–46. Nofsinger, John R. 2001. The Impact of Public Information on Investors. Journal of Banking and Finance 25, 1139–366. Short-Term Price Drift Park, Jinwoo. 1995. A Market Microstructure Explanation for Predictable Variations in Stock Returns Following Large Price Changes. Journal of Financial and Quantitative Analysis 30, 241–56. Pritamani, Mahesh, and Vijay Singal. 2001. Return Predictability Following Large Price Changes and Information Releases. Journal of Banking and Finance 25(4), 631–56. Ryan, Paul, and Richard J. Taffler. 2002. What Firm Specific News Releases Drive Economically Significant Stock Returns and Trading Volumes?
bank run, barriers to entry, bash_history, Bernie Madoff, Flash crash, housing crisis, index fund, locking in a profit, London Whale, market microstructure, merger arbitrage, prediction markets, price discovery process, Sergey Aleynikov, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, zero day
Greenlight Capital, one of the hedge funds lionized in Lewis’ book The Big Short and one of the investors in IEX, recently wrote to its customers that the schemes alleged in Flash Boys “don’t significantly impact us.” In the end, then, let’s not pretend that just because some hedge fund managers claim to believe something, the rest of us must believe it. As you’d expect, they are looking out for their own interests, not ours. Conspiracy of Press Releases “The game is now clear to me,” Brad said. “There’s not a press release I don’t understand.” Lewis presents us with a curious standard: if one can understand press releases, it qualifies one as an expert in capital market microstructure. More curious still is the understanding of press releases that is required. Apparently, every press release is a coded message from the high-frequency conspiracy. According to Lewis, NASDAQ’s missive on their August 22, 2013, outage – “what they said was a technical glitch in the SIP” – wasn’t really about a technical glitch in the SIP. Lewis tells us that the outage was really caused by high-frequency traders because NASDAQ spent money on co-location facilities.
Trend Commandments: Trading for Exceptional Returns by Michael W. Covel
Albert Einstein, Bernie Madoff, Black Swan, commodity trading advisor, correlation coefficient, delayed gratification, diversified portfolio, en.wikipedia.org, Eugene Fama: efficient market hypothesis, family office, full employment, Lao Tzu, Long Term Capital Management, market bubble, market microstructure, Mikhail Gorbachev, moral hazard, Nick Leeson, oil shock, Ponzi scheme, prediction markets, quantitative trading / quantitative ﬁnance, random walk, Sharpe ratio, systematic trading, the scientific method, transaction costs, tulip mania, upwardly mobile, Y2K
., 2003, p. 416. 4. Michael J. Mauboussin and Kristen Bartholdson, “Integrating the Outliers: Two Lessons from the St. Petersburg Paradox.” The Consilient Observer. Vol. 2, No. 2, January 28, 2003. 5. “Trend Following: Performance, Risk, and Correlation Characteristics.” White Paper, Graham Capital Management. See http://www.grahamcapital.com. Zero-Sum 1. Larry Harris, Trading and Exchanges: Market Microstructure for Practitioners. New York: Oxford University Press, 2003. 2. Larry Harris, “The Winners and Losers of the Zero-Sum Game: The Origins of Trading Profits, Price Efficiency and Market Liquidity.” Draft 0.911, May 7, 1993. 3. Ibid. 4. Dave Druz interview with Covel, 2011. 5. Ibid. 6. Ibid. 7. Ibid. Crash and Burn 1. George Bernard Shaw, Irish Literary. 2. “Trend Following: Performance, Risk, and Correlation Characteristics.”
Affordable Care Act / Obamacare, Airbnb, Al Roth, Black Swan, buy low sell high, Credit Default Swap, cross-subsidies, crowdsourcing, disintermediation, diversified portfolio, experimental economics, George Akerlof, Goldman Sachs: Vampire Squid, income inequality, index fund, Jean Tirole, Lean Startup, Lyft, Mark Zuckerberg, market microstructure, Martin Wolf, McMansion, Menlo Park, moral hazard, multi-sided market, Network effects, patent troll, Paul Graham, Peter Thiel, pez dispenser, ride hailing / ride sharing, Sand Hill Road, sharing economy, Silicon Valley, social graph, supply-chain management, TaskRabbit, The Market for Lemons, too big to fail, trade route, transaction costs, two-sided market, Uber for X, ultimatum game, Y Combinator
See “Home Buyers and Sellers Survey Shows Lingering Impact of Tight Credit” (National Association of Realtors, press release, November 13, 2013). 10.Brooks Barnes and Hunter Atkins, “Hollywood’s Old-Time Star Makers Are Swooping In on YouTube’s Party,” New York Times, September 15, 2014; and Katherine Rosman, “Grumpy Cat Has an Agent, and Now a Movie Deal,” Wall Street Journal, May 31, 2013. 11.Noam Cohen, “When Stars Twitter, a Ghost May Be Lurking,” New York Times, March 26, 2009; and Evan Dashevsky, “Who’s Writing Your Favorite Celebrity’s Tweets,” PC World, November 2013. 12.Daniel F. Spulber, Market Microstructure: Intermediaries and the Theory of the Firm (New York: Cambridge University Press, 1999), 21. 13.E-mail correspondence with Daniel Spulber, September 28, 2011. See also Daniel F. Spulber, “Should Business Method Inventions Be Patentable?” Journal of Legal Analysis 3, no. 1(2011): 279. 14.Interview with Mike Maples Jr., September 17, 2014. 15.The notion that middlemen accelerate connections might be called the catalyst view of middlemen.
The Misbehavior of Markets by Benoit Mandelbrot
Albert Einstein, asset allocation, Augustin-Louis Cauchy, Benoit Mandelbrot, Big bang: deregulation of the City of London, Black-Scholes formula, British Empire, Brownian motion, buy low sell high, capital asset pricing model, carbon-based life, discounted cash flows, diversification, double helix, Edward Lorenz: Chaos theory, Elliott wave, equity premium, Eugene Fama: efficient market hypothesis, Fellow of the Royal Society, full employment, Georg Cantor, Henri Poincaré, implied volatility, index fund, informal economy, invisible hand, John von Neumann, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, market bubble, market microstructure, new economy, paper trading, passive investing, Paul Lévy, Plutocrats, plutocrats, price mechanism, quantitative trading / quantitative ﬁnance, Ralph Nelson Elliott, RAND corporation, random walk, risk tolerance, Robert Shiller, Robert Shiller, short selling, statistical arbitrage, statistical model, Steve Ballmer, stochastic volatility, transfer pricing, value at risk, volatility smile
Zoom in on the fast episodes, and they are seen to have sub-clusters of fast and slow sub-intervals—clusters within clusters within clusters. It is a classic multifractal pattern. Its scaling stretches, through every focal length of our mathematical zoom lens, from about two hours to 180 days—an unusually long zone of regularity. At shorter time-intervals, a new pattern emerges: What economists call market “microstructure” starts to kick in. Here, the average price change is up or down by just 0.14 pfennig, only twice the spread of 0.7 pfennig between bid and ask. With such narrow profit opportunities, some traders do not bother changing their quotes instantly, so you would expect the data to look differently. At intervals longer than 180 days, yet another effect alters the data stream. The Noah Effect is fading.
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, 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, passive investing, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, statistical model, systematic trading, technology bubble, the market place, Thomas Kuhn: the structure of scientific revolutions, transaction costs, value at risk, yield curve
“Market Timing Ability and Volatility Implied in Investment Newsletters’ Asset Allocation Recommendations.” Journal of Financial Economics 42, Issue 3 (November 1996): 397–421. Bibliography 281 Grubel, Herbert. “Internationally Diversified Portfolio: Welfare Gains and Capital Flows.” The American Economic Review Vol. 58, No. 5 (December 1968): 1299–1314. Harris, L. Trading and Exchanges: Market Microstructure for Practitioners. New York: Oxford University Press, 2003. Hill, J.M., V. Balasubramanian, K. Gregory, and I. Tierens. “Finding Alpha via Covered Index Writing.” Financial Analysts Journal 62, No. 5 (Sept./Oct. 2006): 29–46. Hryshko, D., M.J. Luengo-Prado, and B.E. Sorensen. “Childhood Determinants of Risk Aversion.” www.ssrn.com, 2009. Ibbotson, R.G., and P.D. Kaplan. “Does Asset Allocation Policy Explain 40, 90, or 100% of Performance?”
Python for Finance by Yuxing Yan
asset-backed security, business intelligence, capital asset pricing model, constrained optimization, correlation coefficient, distributed generation, diversified portfolio, implied volatility, market microstructure, P = NP, p-value, quantitative trading / quantitative ﬁnance, Sharpe ratio, time value of money, value at risk, volatility smile
Yan has actively done research with several publications in Journal of Accounting and Finance, Journal of Banking and Finance, Journal of Empirical Finance, Real Estate Review, Pacific Basin Finance Journal, Applied Financial Economics, and Annals of Operations Research. For example, his latest publication, co-authored with Shaojun Zhang, will appear in the Journal of Banking and Finance in 2014. His research areas include investment, market microstructure, and open source finance. He is proficient at several computer languages such as SAS, R, MATLAB, C, and Python. From 2003 to 2010, he worked as a technical director at Wharton Research Data Services (WRDS), where he debugged several hundred computer programs related to research for WRDS users. After that, he returned to teaching in 2010 and introduced R into several quantitative courses at two universities.
algorithmic trading, automated trading system, banking crisis, bash_history, Bernie Madoff, butterfly effect, buttonwood tree, cloud computing, collapse of Lehman Brothers, Donald Trump, Flash crash, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, Joseph Schumpeter, latency arbitrage, Long Term Capital Management, Mark Zuckerberg, market design, market microstructure, pattern recognition, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, Sergey Aleynikov, Small Order Execution System, South China Sea, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stochastic process, transaction costs, Watson beat the top human players on Jeopardy!
He wasn’t ready to take on the whole system—yet. Matt Andresen also spoke at the conference. The year before, the onetime Island CEO had left his high-paying job at Citadel to launch his own computer-trading outfit in Chicago, Headlands Technologies. Andresen told his audience that top-shelf traders today need to know much more than quant strategies—they also need to have a deep understanding of market microstructure. They need to know the plumbing. Another speaker was Andrei Kirilenko, who’d conducted in-depth research into the Flash Crash for the Commodity Futures Trading Commission. Kirilenko had discovered that high-speed gunners typically traded in the direction of the price movement of a stock for the first five seconds of a move, then flipped and traded in the opposite direction after ten seconds.
Matchmakers: The New Economics of Multisided Platforms by David S. Evans, Richard Schmalensee
Airbnb, big-box store, business process, cashless society, Deng Xiaoping, if you build it, they will come, Internet Archive, invention of movable type, invention of the printing press, invention of the telegraph, invention of the telephone, Jean Tirole, Lyft, M-Pesa, market friction, market microstructure, mobile money, multi-sided market, Network effects, Productivity paradox, profit maximization, purchasing power parity, ride hailing / ride sharing, sharing economy, Silicon Valley, Snapchat, Steve Jobs, Tim Cook: Apple, transaction costs, two-sided market, Uber for X, Victor Gruen, winner-take-all economy
option=com_content&id=1051:family-a-fortune. 4. Malcom Gladwell, “The Terrazzo Jungle,” New Yorker, March 15, 2004, http://www.newyorker.com/magazine/2004/03/15/the-terrazzo-jungle; M. Jeffery Hardwick, Mall Maker (Philadelphia: University of Pennsylvania Press, 2010). 5. Market makers are sometimes known as liquidity providers, and buyers and sellers are sometimes known as liquidity takers. See Larry Harris, Trading & Exchanges: Market Microstructure for Practitioners (Oxford, UK: Oxford University Press, 2002). 6. In the United States, regulation drove the shift to decimalization (tick sizes of one cent). See US Securities and Exchange Commission, “Report to Congress on Decimalization,” July 2012, 4–6, https://www.sec.gov/news/studies/2012/decimalization-072012.pdf. 7. For a very clear discussion of the economics of tick size rules and for details omitted here in the interest of simplicity, see James J.
Reinventing the Bazaar: A Natural History of Markets by John McMillan
accounting loophole / creative accounting, Albert Einstein, Andrei Shleifer, Anton Chekhov, Asian financial crisis, congestion charging, corporate governance, crony capitalism, Dava Sobel, Deng Xiaoping, experimental economics, experimental subject, fear of failure, first-price auction, frictionless, frictionless market, George Akerlof, George Gilder, global village, Hernando de Soto, I think there is a world market for maybe five computers, income inequality, income per capita, informal economy, invisible hand, Isaac Newton, job-hopping, John Harrison: Longitude, John von Neumann, land reform, lone genius, manufacturing employment, market clearing, market design, market friction, market microstructure, means of production, Network effects, new economy, offshore financial centre, pez dispenser, pre–internet, price mechanism, profit maximization, profit motive, proxy bid, purchasing power parity, Ronald Coase, Ronald Reagan, sealed-bid auction, second-price auction, Silicon Valley, spectrum auction, Stewart Brand, The Market for Lemons, The Nature of the Firm, The Wealth of Nations by Adam Smith, trade liberalization, transaction costs, War on Poverty, Xiaogang Anhui farmers, yield management
Sobel, Dava. 1996. Longitude. New York, Penguin. Sobel, Joel, and Takahashi, Ichiro. 1983. “A Multistage Model of Bargaining.” Review of Economic Studies 50, 411–426. Sobel, Robert. 1970. The Curbstone Brokers: The Origins of the American Stock Exchange. New York, Macmillan. Spence, A Michael. 1973. “Job Market Signaling.” Quarterly Journal of Economics 87, 355–374. Spulber, Daniel F. 1996. “Market Microstructure and Intermediation.” Journal of Economic Perspectives 10, 135–152. Squires, Dale, Kirkley, James, and Tisdell, Clement A. 1995. “Individual Transferable Quotas as a Fisheries Management Tool.” Reviews in Fisheries Science 3, 141–169. Steinbeck, John. 1996. Sweet Thursday. New York, Penguin. Stigler, George J. 1961. “The Economics of Information.” Journal of Political Economy 69, 213–225.
Red-Blooded Risk: The Secret History of Wall Street by Aaron Brown, Eric Kim
Albert Einstein, algorithmic trading, Asian financial crisis, Atul Gawande, backtesting, Basel III, Benoit Mandelbrot, Bernie Madoff, Black Swan, capital asset pricing model, central bank independence, Checklist Manifesto, corporate governance, credit crunch, Credit Default Swap, disintermediation, distributed generation, diversification, diversified portfolio, 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: store of value / unit of account / medium of exchange, moral hazard, natural language processing, open economy, 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, The Myth of the Rational Market, too big to fail, transaction costs, value at risk, yield curve
(Of course, as Ken French and John Bogle independently pointed out to me, half the nonindex investors must be even more overweighted in the overpriced assets, and all the nonindex investors pay higher costs.) Large, diversified portfolios have also been blamed for investors not providing oversight to their investments, and for feeding bubbles and crashes. The MPT focus on returns measured periodically, mainly monthly, may have led to underappreciation for both long-term economics and short-term market microstructure. Hedge funds are much better than index funds at determining fundamental value, at providing oversight, for operating at a variety of time scales from microseconds to decades, for reining in bubbles, and at rushing in to repair after crashes. Of course, just because hedge funds can do these things it doesn’t follow that all, or even most, hedge funds actually do these things. In the real world, hedge funds were suppressed by theories that said they shouldn’t be able to make money and were overpriced.
Albert Einstein, Asian financial crisis, asset allocation, asset-backed security, backtesting, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, business process, capital asset pricing model, capital controls, central bank independence, collateralized debt obligation, 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, Hyman Minsky, implied volatility, index fund, inflation targeting, interest rate swap, inventory management, invisible hand, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, market microstructure, moral hazard, 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, Sharpe ratio, short selling, sovereign wealth fund, special drawing rights, statistical arbitrage, stochastic volatility, The Great Moderation, time value of money, too big to fail, transaction costs, unbiased observer, value at risk, Vanguard fund, yield curve
Speculative flows change the term structure of a market, which, in turn, changes the reaction function of a producer or storage operator. Commodity markets now tend to gap more quickly, showing evidence of what I call “single point volatility.” And there is some evidence of a greater prevalence of serial correlation in pricing, so trends are established much more quickly. This doesn’t necessarily mean commodities are riskier, but it is a change in the market microstructure that you have to stay on top of. As a result of spot shortages and outages, is it easier to be long commodities than short? Although none of this could be described as “easy,” each manager may have a different comfort zone, which is a function of how trades are structured, what the asymmetry is, the risk versus reward, etc. However, because of the index component in the marketplace, recently it appears that bull trend moves tend to be much more exaggerated over time than bear trend moves.
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, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, central bank independence, collateralized debt obligation, 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, George Akerlof, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, interest rate swap, invisible hand, Kenneth Rogoff, laissez-faire capitalism, law of one price, 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, New Journalism, oil shock, p-value, passive investing, 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, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, 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
Takeaways from the vast literature on equity momentum • As noted above, past performance dependence across equities includes a multi-month momentum pattern as well as short-term and long-term reversal patterns. On paper, the strongest result is that past month winners tend to strongly underperform past month losers over the subsequent month. This first-month reversal effect is sometimes attributed to market microstructure effects such as bid–ask bounce, or to price overreaction to firm-specific news, but the most compelling explanation is price concession caused by large trades. Liquidity providers facing low trading costs can exploit this pattern. Return reversals are even stronger over shorter horizons (day and week), for less liquid stocks, when using industry-adjusted returns, and amidst high volatility
Derivatives Markets by David Goldenberg
Black-Scholes formula, Brownian motion, capital asset pricing model, commodity trading advisor, compound rate of return, conceptual framework, 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, Norbert Wiener, 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, Y2K, yield curve, zero-coupon bond
Some of the highlights of the text are: 1. an emphasis on the quote mechanism, and understanding where to ﬁnd and how to read and interpret the data that underlies this ﬁeld; 2. an early presentation of the hedging role of forward contracts in Chapter 2, with the use of Microsoft Excel charts as visual aids; 3. an early emphasis on FX markets to develop a global perspective, as opposed to the usual stock market focus; 4. separating out forward contract valuation in the no-dividend case from the dividend case, as exempliﬁed in Chapters 3 and 4; 5. recognizing the alternative derivative valuation problems: at initiation, at expiration, and at an intermediate time; 6. an emphasis on market microstructure in Chapter 5 on futures markets, with due attention to the limit order book and Globex; 7. a portfolio approach to hedging with futures contracts in Chapter 6, with a discussion of most of the approaches to hedging, including carrying charge hedging; xxxiv PREFACE 8. discussion of difﬁcult to explain, yet important concepts such as storage, the price of storage, and the all-important spreads notion; 9. an extensive Chapter 7 on ﬁnancial futures contracts, with particular emphasis on Eurodollar spot and futures, since these are the basis for understanding swaps in Chapter 8; 10. a complete discussion of stock index futures in Chapter 8, and their uses in alternative hedging strategies.
accounting loophole / creative accounting, banking crisis, banks create money, barriers to entry, Benoit Mandelbrot, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, butterfly effect, capital asset pricing model, cellular automata, central bank independence, citizen journalism, clockwork universe, collective bargaining, complexity theory, correlation coefficient, credit crunch, David Ricardo: comparative advantage, debt deflation, diversification, double entry bookkeeping, en.wikipedia.org, Eugene Fama: efficient market hypothesis, experimental subject, Financial Instability Hypothesis, Fractional reserve banking, full employment, Henri Poincaré, housing crisis, Hyman Minsky, income inequality, invisible hand, iterative process, John von Neumann, laissez-faire capitalism, liquidity trap, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market clearing, market microstructure, means of production, minimum wage unemployment, open economy, place-making, Ponzi scheme, profit maximization, quantitative easing, RAND corporation, random walk, risk tolerance, risk/return, Robert Shiller, Robert Shiller, Ronald Coase, Schrödinger's Cat, scientific mainstream, seigniorage, six sigma, South Sea Bubble, stochastic process, The Great Moderation, The Wealth of Nations by Adam Smith, Thorstein Veblen, time value of money, total factor productivity, tulip mania, wage slave
C. (2007) ‘Reserve requirement systems in OECD countries,’ SSRN eLibrary. Oda, S. H., K. Miura, K. Ueda and Y. Baba (2000) ‘The application of cellular automata and agent models to network externalities in consumers’ theory: a generalization of life game,’ in W. A. Barnett, C. Chiarella, S. Keen, R. Marks and H. Schnabl (eds), Commerce, Complexity and Evolution, New York: Cambridge University Press. O’Hara, M. (1995) Market Microstructure Theory, Cambridge: Blackwell. Ormerod, P. (1997) The Death of Economics, 2nd edn, New York: John Wiley & Sons. Ormerod, P. (2001) Butterfly Economics: A New General Theory of Social and Economic Behavior, London: Basic Books. Ormerod, P. (2004) ‘Neoclassical economic theory: a special and not a general case,’ in E. Fullbrook (ed.), A Guide to What’s Wrong with Economics, London: Anthem Press, pp. 41–6.