The Econometrics of Financial Markets / Edition 1 available in Hardcover, eBook
The Econometrics of Financial Markets / Edition 1
- ISBN-10:
- 0691043019
- ISBN-13:
- 9780691043012
- Pub. Date:
- 12/29/1996
- Publisher:
- Princeton University Press
- ISBN-10:
- 0691043019
- ISBN-13:
- 9780691043012
- Pub. Date:
- 12/29/1996
- Publisher:
- Princeton University Press
The Econometrics of Financial Markets / Edition 1
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Overview
Recent decades have seen an extraordinary growth in the use of quantitative methods in financial markets. Finance professionals routinely use sophisticated statistical techniques in portfolio management, proprietary trading, risk management, financial consulting, and securities regulation. This graduate-level textbook is designed for PhD students, advanced MBA students, and industry professionals interested in the econometrics of financial modeling. The book covers the entire spectrum of empirical finance, including the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory, the term structure of interest rates, dynamic models of economic equilibrium, and nonlinear financial models such as ARCH, neural networks, statistical fractals, and chaos theory.
Each chapter develops statistical techniques within the context of a particular financial application. This exciting text contains a unique and accessible combination of theory and practice, bringing state-of-the-art statistical techniques to the forefront of financial applications. Each chapter also includes a discussion of recent empirical evidence, for example, the rejection of the Random Walk Hypothesis, as well as problems designed to help readers incorporate what they have learned into their own applications.
Product Details
ISBN-13: | 9780691043012 |
---|---|
Publisher: | Princeton University Press |
Publication date: | 12/29/1996 |
Edition description: | New Edition |
Pages: | 632 |
Product dimensions: | 6.00(w) x 9.25(h) x (d) |
About the Author
Table of Contents
List of Figures xiiiList of Tables xvPreface xix
1Introduction 31.1 Organization of the Book 41.2 Useful Background 61.2.1 Mathematics Background 61.2.2 Probability and Statistics Background 61.2.3 Finance Theory Background 71.3 Notation 81.4 Prices, Returns, and Compounding 91.4.1 Definitions and Conventions 91.4.2 The Marginal, Conditional, and Joint Distribution of Returns 131.5 Market Efficiency 201.5.1 Efficient Markets and the Law of Iterated Expectations 221.5.2 Is Market Efficiency Testable? 24
2The Predictability of Asset Returns 272.1 The Random Walk Hypotheses 282.1.1 The Random Walk 1: IID Increments 312.1.2 The Random Walk 2: Independent Increments 322.1.3 The Random Walk 3: Uncorrelated Increments 332.2 Tests of Random Walk 1: IID Increments 332.2.1 Traditional Statistical Tests 332.2.2 Sequences and Reversals, and Runs 342.3 Tests of Random Walk 2: Independent Increments 412.3.1 Filter Rules 422.3.2 Technical Analysis 432.4 Tests of Random Walk 3: Uncorrelated Increments 442.4.1 Autocorrelation Coefficients 442.4.2 Portmanteau Statistics 472.4.3 Variance Ratios 482.5 Long-Horizon Returns 552.5.1 Problems with Long-Horizon Inferences 572.6 Tests For Long-Range Dependence 592.6.1 Examples of Long-Range Dependence 592.6.2 The Hurst-Mandelbrot Rescaled Range Statistic 622.7 Unit Root Tests 642.8 Recent Empirical Evidence 652.8.1 Autocorrelations 662.8.2 Variance Ratios 682.8.3 Cross-Autocorrelations and Lead-Lag Relations 742.8.4 Tests Using Long-Horizon Returns 782.9 Conclusion 80
3Market Microstructure 833.1 Nonsynchronous Trading 843.1.1 A Model of Nonsynchronous Trading 853.1.2 Extensions and Generalizations 983.2 The Bid-Ask Spread 993.2.1 Bid-Ask Bounce 1013.2.2 Components of the Bid-Ask Spread 1033.3 Modeling Transactions Data 1073.3.1 Motivation 1083.3.2 Rounding and Barrier Models 1143.3.3 The Ordered Probit Model 1223.4 Recent Empirical Findings 1283.4.1 Nonsynchronous Trading 1283.4.2 Estimating the Effective Bid-Ask Spread 1343.4.3 Transactions Data 1363.5 Conclusion 144
4Event-Study Analysis 1494.1 Outline of an Event Study 1504.2 An Example of an Event Study 1524.3 Models for Measuring Normal Performance 1534.3.1 Constant-Mean-Return Model 1544.3.2 Market Model 1554.3.3 Other Statistical Models 1554.3.4 Economic Models 1564.4 Measuring and Analyzing Abnormal Returns 1574.4.1 Estimation of the Market Model 1584.4.2 Statistical Properties of Abnormal Returns 1594.4.3 Aggregation of Abnormal Returns 1604.4.4 Sensitivity to Normal Return Model 1624.4.5 CARs for the Earnings-Announcement Example 1634.4.6 Inferences with Clustering 1664.5 Modifying the Null Hypothesis 1674.6 Analysis of Power 1684.7 Nonparametric Tests 1724.8 Cross-Sectional Models 1734.9 Further Issues 1754.9.1 Role of the Sampling Interval 1754.9.2 Inferences with Event-Date Uncertainty 1764.9.3 Possible Biases 1774.10 Conclusion 178
5The Capital Asset Pricing Model 1815.1 Review of the CAPM 1815.2 Results from Efficient-Set Mathematics 1845.3 Statistical Framework for Estimation and Testing 1885.3.1 Sharpe-Lintner Version 1895.3.2 Black Version 1965.4 Size of Tests 2035.5 Power of Tests 2045.6 Nonnormal and Non-IID Returns 2085.7 Implementation of Tests 2115.7.1 Summary of Empirical Evidence 2115.7.2 Illustrative Implementation 2125.7.3 Unobservability of the Market Portfolio 2135.8 Cross-Sectional Regressions 2155.9 Conclusion 217
6Multifactor Pricing Models 2196.1 Theoretical Background 2196.2 Estimation and Testing 2226.2.1 Portfolios as Factors with a Riskfree Asset 2236.2.2 Portfolios as Factors without a Riskfree Asset 2246.2.3 Macroeconomic Variables as Factors 2266.2.4 Factor Portfolios Spanning the Mean-Variance\protect\\ Frontier 2286.3 Estimation of Risk Premia and Expected Returns 2316.4 Selection of Factors 2336.4.1 Statistical Approaches 2336.4.2 Number of Factors 2386.4.3 Theoretical Approaches 2396.5 Empirical Results 2406.6 Interpreting Deviations from Exact Factor Pricing 2426.6.1 Exact Factor Pricing Models, Mean-Variance Analysis, and the Optimal Orthogonal Portfolio 2436.6.2 Squared Sharpe Ratios 2456.6.3 Implications for Separating Alternative Theories 2466.7 Conclusion 251
7Present-Value Relations 2537.1 The Relation between Prices, Dividends, and Returns 2547.1.1 The Linear Present-Value Relation with Constant Expected Returns 2557.1.2 Rational Bubbles 2587.1.3 An Approximate Present-Value Relation with Time-Varying Expected Returns 2607.1.4 Prices and Returns in a Simple Example 2647.2 Present-Value Relations and US Stock Price Behavior 2677.2.1 Long-Horizon Regressions 2677.2.2 Volatility Tests 2757.2.3 Vector Autoregressive Methods 2797.3 Conclusion 286
8Intertemporal Equilibrium Models 2918.1 The Stochastic Discount Factor 2938.1.1 Volatility Bounds 2968.2 Consumption-Based Asset Pricing with Power Utility 3048.2.1 Power Utility in a Lognormal Model 3068.2.2 Power Utility and Generalized Method of\protect\\ Moments 3148.3 Market Frictions 3148.3.1 Market Frictions and Hansen-Jagannathan\protect\\ Bounds 3158.3.2 Market Frictions and Aggregate Consumption\protect\\ Data 3168.4 More General Utility Functions 3268.4.1 Habit Formation 3268.4.2 Psychological Models of Preferences 3328.5 Conclusion 334
9Derivative Pricing Models 3399.1 Brownian Motion 3419.1.1 Constructing Brownian Motion 3419.1.2 Stochastic Differential Equations 3469.2 A Brief Review of Derivative Pricing Methods 3499.2.1 The Black-Scholes and Merton Approach 3509.2.2 The Martingale Approach 3549.3 Implementing Parametric Option Pricing Models 3559.3.1 Parameter Estimation of Asset Price Dynamics 3569.3.2 Estimating $\sigma $ in the Black-Scholes Model 3619.3.3 Quantifying the Precision of Option Price Estimators 3679.3.4 The Effects of Asset Return Predictability 3699.3.5 Implied Volatility Estimators 3779.3.6 Stochastic Volatility Models 3799.4 Pricing Path-Dependent Derivatives Via Monte Carlo Simulation 3829.4.1 Discrete Versus Continuous Time 3839.4.2 How Many Simulations to Perform 3849.4.3 Comparisons with a Closed-Form Solution 3849.4.4 Computational Efficiency 3869.4.5 Extensions and Limitations 3909.5 Conclusion 391
10Fixed-Income Securities 39510.1 Basic Concepts 39610.1.1 Discount Bonds 39710.1.2 Coupon Bonds 40110.1.3 Estimating the Zero-Coupon Term Structure 40910.2 Interpreting the Term Structure of Interest Rates 41310.2.1 The Expectations Hypothesis 41310.2.2 Yield Spreads and Interest Rate Forecasts 41810.3 Conclusion 423
11Term-Structure Models 42711.1 Affine-Yield Models 42811.1.1 A Homoskedastic Single-Factor Model 42911.1.2 A Square-Root Single-Factor Model 43511.1.3 A Two-Factor Model 43811.1.4 Beyond Affine-Yield Models 44111.2 Fitting Term-Structure Models to the Data 44211.2.1 Real Bonds, Nominal Bonds, and Inflation 44211.2.2 Empirical Evidence on Affine-Yield Models 44511.3 Pricing Fixed-Income Derivative Securities 45511.3.1 Fitting the Current Term Structure Exactly 45611.3.2 Forwards and Futures 45811.3.3 Option Pricing in a Term-Structure Model 46111.4 Conclusion 464
12Nonlinearities in Financial Data 46712.1 Nonlinear Structure in Univariate Time Series 46812.1.1 Some Parametric Models 47012.1.2 Univariate Tests for Nonlinear Structure 47512.2 Models of Changing Volatility 47912.2.1 Univariate Models 48112.2.2 Multivariate Models 49012.2.3 Links between First and Second Moments 49412.3 Nonparametric Estimation 49812.3.1 Kernel Regression 50012.3.2 Optimal Bandwidth Selection 50212.3.3 Average Derivative Estimators 50412.3.4 Application: Estimating State-Price Densities 50712.4 Artificial Neural Networks 51212.4.1 Multilayer Perceptrons 51212.4.2 Radial Basis Functions 51612.4.3 Projection Pursuit Regression 51812.4.4 Limitations of Learning Networks 51812.4.5 Application: Learning the Black-Scholes Formula 51912.5 Overfitting and Data-Snooping 52312.6 Conclusion 524
Appendix 527A.1 Linear Instrumental Variables 527A.2 Generalized Method of Moments 532A.3 Serially Correlated and Heteroskedastic Errors 534A.4 GMM and Maximum Likelihood 536
References 541Author Index 587Subject Index 597