Empirical Asset Pricing
Models and Methods
by Ferson
ISBN: 9780262039376 | Copyright 2019
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An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments.
This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics.
The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
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Contents (pg. v) | |
Preface (pg. xiii) | |
Introduction (pg. 1) | |
I. INTRODUCTION TO EMPIRICAL ASSET PRICING (pg. 7) | |
1. Stochastic Discount Factors and m-Talk (pg. 9) | |
1.1 The Main Equation (1.1) (pg. 9) | |
1.2 From the General to the Specific (pg. 11) | |
1.3 Arbitrage and the SDF (pg. 11) | |
2. State Pricing (pg. 13) | |
2.1 Basic Setup (pg. 13) | |
2.2 State Pricing with a Representative Maximizing Agent (pg. 14) | |
2.3 Heterogeneous Agents with Complete Markets& (pg. 15) | |
2.4 Complete Markets, Risk Sharing, and a Constructed Representative Agent (pg. 16) | |
2.5 Risk-Neutral Probabilities (pg. 17) | |
2.6 Recovery (pg. 18) | |
2.7 Multiperiod m-Talk and State Pricing (pg. 19) | |
3. Maximization and the m-Talk Euler Equation (pg. 23) | |
3.1 General Statement (pg. 23) | |
3.2 The Consumption CAPM (pg. 24) | |
3.3 Finding m Using a Perturbation (pg. 25) | |
4. Expected Risk Premiums and Alphas (pg. 27) | |
4.1 General Statement (pg. 27) | |
4.2 The SDF Alphas and Abnormal Returns (pg. 28) | |
4.3 Relation to Beta-Pricing: The Maximum Correlation Portfolio (pg. 29) | |
4.4 Applications of Alpha (pg. 29) | |
4.5 Volatility Risk Premiums (pg. 30) | |
5. So Many Models, So Little Time (pg. 33) | |
5.1 Polynomials in Consumption or Wealth (pg. 33) | |
5.2 Habits and Durability (pg. 36) | |
5.3 State Nonseparability (pg. 38) | |
5.4 Heterogeneity (pg. 40) | |
5.5 General Affine Models (pg. 41) | |
5.6 Ambiguity Aversion and Knightian Uncertainty (pg. 41) | |
5.7 Behavioral SDFs (pg. 42) | |
5.8 Multigood Models (pg. 43) | |
5.9 Representation Using Future Consumption and Returns (pg. 44) | |
6. Applications of m-Talk (pg. 45) | |
6.1 Real Interest Rates (pg. 45) | |
6.2 Term Structure (pg. 47) | |
6.3 Real and Nominal m-Talk (pg. 49) | |
6.4 International m-Talk (pg. 50) | |
6.5 Conditional Asset Pricing (pg. 53) | |
6.6 Market Efficiency (pg. 54) | |
6.7 Investment Performance Evaluation (pg. 55) | |
7. Three Paradigms of Empirical Asset Pricing (pg. 57) | |
7.1 Minimum Variance Efficiency (pg. 57) | |
7.2 Beta Pricing (pg. 58) | |
7.3 Relation among the Three Paradigms (pg. 58) | |
II. MEAN VARIANCE MODELS (pg. 63) | |
8. Mean Variance Analysis (pg. 65) | |
8.1 Deriving the Minimum Variance Frontier: The Classics (pg. 65) | |
8.2 The Minimum Variance Boundary (pg. 66) | |
8.3 The Hansen and Richard Representation (pg. 72) | |
8.4 Generalizations, Extensions, and Evidence (pg. 73) | |
9. Mean Variance Efficiency with Conditioning Information (pg. 79) | |
9.1 Conditional and Unconditional Efficiency with Information (pg. 79) | |
9.2 Relation to Utility Maximization (pg. 83) | |
9.3 Explicit UE Solutions (pg. 83) | |
9.4 Extensions (pg. 88) | |
9.5 Empirical Evidence (pg. 89) | |
10. Variance Bounds (pg. 95) | |
10.1 A Simple Development (pg. 95) | |
10.2 Duality with the Usual Mean-Variance Diagram (pg. 96) | |
10.3 Computing the HJ Bounds (pg. 99) | |
10.4 Drawing Inferences with HJ Bounds (pg. 100) | |
10.5 Related Measures and Bounds (pg. 100) | |
11. Variance Bounds with Conditioning Information (pg. 107) | |
11.1 Refining HJ Bounds with Conditioning Information (pg. 107) | |
11.2 Empirical Application (pg. 110) | |
11.3 Sampling Performance (pg. 114) | |
11.4 Practical Summary (pg. 118) | |
III. MULTIBETA PRICING (pg. 121) | |
12. Arbitrage Pricing and Factor Analysis (pg. 123) | |
12.1 Overview (pg. 123) | |
12.2 Limits to Arbitrage and Costly Arbitrage (pg. 128) | |
12.3 Factor Analysis (pg. 130) | |
13. Multibeta Equilibrium Models (pg. 137) | |
13.1 Multiperiod Consumption-Investment Problems (pg. 137) | |
13.2 The Classical Multibeta Model (pg. 139) | |
14. Multibeta Models with Conditioning Information (pg. 145) | |
14.1 Conditional and Unconditional Multifactor Efficiency (pg. 145) | |
14.2 The Three Paradigms of Empirical Asset Pricing, Revisited& (pg. 149) | |
IV. EMPIRICAL ASSET PRICING TOOLS (pg. 155) | |
15. Introduction to the Generalized Method of Moments (pg. 157) | |
15.1 Motivation and General Setup (pg. 157) | |
15.2 Simple Examples (pg. 159) | |
16. GMM Implementation (pg. 163) | |
16.1 Overview (pg. 163) | |
16.2 Exactly Identified Models (pg. 164) | |
16.3 Linear Models (pg. 165) | |
16.4 Nonlinear, Overidentified Models (pg. 165) | |
16.5 One Step, Two Steps, N Steps, or More . . . (pg. 166) | |
16.6 Search Problems (pg. 167) | |
16.7 The Asymptotic Covariance Matrix: A First Cut (pg. 168) | |
17. Covariance Matrices for the GMM (pg. 169) | |
17.1 Autocorrelated Moment Conditions (pg. 169) | |
17.2 Optimal Weighting Matrices for Autocorrelated Moment Conditions (pg. 174) | |
17.3 A Catalog of Consistent Covariance Matrices (pg. 175) | |
17.4 Alternative Non-optimal Weighting Matrices (pg. 176) | |
17.5 Asymptotic Variance for GMM When Non-optimal Weighting Matrices Are Used (pg. 176) | |
18. GMM Tests (pg. 179) | |
18.1 An Aside on Chi-Square Variables (pg. 179) | |
18.2 Hansen’s J-Statistic (pg. 179) | |
18.3 Asymptotic Wald Tests and the Delta Method (pg. 180) | |
18.4 Hypotheses about Pricing Errors (pg. 181) | |
18.5 Conditional Moment Encompassing Tests (pg. 182) | |
18.6 Other Tests (pg. 183) | |
19. Advanced GMM (pg. 185) | |
19.1 Scaling Factors in GMM (pg. 185) | |
19.2 Optimal Instrument Choice (pg. 186) | |
19.3 Weak Instruments (pg. 188) | |
19.4 Missing Data (pg. 189) | |
19.5 Simulated Method of Moments (pg. 189) | |
19.6 Testing Inequality Restrictions (pg. 190) | |
20. GMM Examples (pg. 193) | |
20.1 Examples Illustrating the Generality of the GMM (pg. 193) | |
20.2 Formulating Asset Pricing Models with GMM& (pg. 195) | |
21. Multivariate Regression Methods (pg. 205) | |
21.1 Regression Model Restrictions (pg. 205) | |
21.2 Testing Portfolio Efficiency in the Multivariate Regression Model (pg. 208) | |
21.3 Economic Interpretation (pg. 211) | |
21.4 Some Results from Gibbons, Ross, and Shanken (pg. 214) | |
21.5 MacKinlay’s Power Analysis and the Fama-French Factors (pg. 215) | |
21.6 Finite-Sample Results (pg. 217) | |
22. Cross-Sectional Regression (pg. 219) | |
22.1 CSR Tests (pg. 219) | |
22.2 Everything, Including CSR, Is a Special Case of GMM (pg. 224) | |
22.3 Testing Unconditional Models with CSR Methods& (pg. 224) | |
22.4 Errors in the Betas (pg. 228) | |
22.5 Asymptotic Distributions for CSR Coefficients& (pg. 230) | |
22.6 Conditional Asset Pricing with CSR (pg. 230) | |
22.7 CSR versus Spread Portfolios (pg. 231) | |
23. Introduction to Panel Methods (pg. 233) | |
23.1 Concepts and Terminology (pg. 233) | |
23.2 Firm Effects and Time Effects (pg. 234) | |
23.3 Standard Errors and Clustering (pg. 235) | |
23.4 Panels in GMM (pg. 237) | |
23.5 How Dummy Variables Work in Panels (pg. 239) | |
23.6 Predictive Panel Regressions (pg. 243) | |
23.7 Random Firm Effects and Efficient Estimation (pg. 248) | |
23.8 Some Results from Petersen (2009) (pg. 249) | |
23.9 Differences in Differences (pg. 250) | |
23.10 Regression Discontinuity (pg. 254) | |
24. Bootstrapping Methods and Multiple Comparisons (pg. 257) | |
24.1 Bootstrap 101 (pg. 257) | |
24.2 Multiple Comparisons (pg. 261) | |
24.3 Bootstrap 201: Inferences in Cross Sections (pg. 266) | |
V. INVESTMENT PERFORMANCE EVALUATION (pg. 273) | |
25. Classical Investment Performance Evaluation (pg. 275) | |
25.1 The Classical Performance Measures (pg. 275) | |
25.2 Properties of the Classical Measures (pg. 284) | |
25.3 The Evidence for Professionally Managed Portfolios Using Classical Measures (pg. 288) | |
26. Conditional Performance Evaluation (pg. 297) | |
26.1 Overview (pg. 297) | |
26.2 Motivating Example (pg. 298) | |
26.3 Conditional Alphas and Betas (pg. 299) | |
26.4 Conditional Market Timing (pg. 300) | |
26.5 Conditional Weight-Based Measures (pg. 302) | |
26.6 Interpreting Conditional Performance Evidence (pg. 303) | |
26.7 Conditional Market Timing Evidence (pg. 303) | |
26.8 Conditional Weight-Based Evidence (pg. 304) | |
26.9 Volatility Timing Evidence (pg. 304) | |
27. Term Structure and Bond Fund Performance (pg. 305) | |
27.1 Background (pg. 305) | |
27.2 Term Structure Model Stochastic Discount Factors (pg. 306) | |
27.3 Single-Factor Model Example (pg. 308) | |
27.4 Addressing Interim Trading Bias (pg. 309) | |
27.5 Empirical Findings (pg. 311) | |
28. Investment Performance Evaluation: A Modern Perspective (pg. 315) | |
28.1 Market Efficiency and Fund Performance (pg. 316) | |
28.2 Troubles with Traditional Alphas (pg. 316) | |
28.3 SDF Alphas and Managers’ Information (pg. 317) | |
28.4 The Appropriate Benchmark (pg. 318) | |
28.5 Mean Variance Efficient Benchmarks Are Almost Never Appropriate Benchmarks (pg. 319) | |
28.6 Justifying the Sharpe Ratio (pg. 320) | |
28.7 Comments on Holdings-Based Performance Measures (pg. 321) | |
28.8 An Example of Holdings-Based Performance with Risk Adjustment (pg. 322) | |
28.9 Summary (pg. 323) | |
VI. SELECTED TOPICS (pg. 325) | |
29. Production-Based Asset Pricing (pg. 327) | |
29.1 Setup and Goals (pg. 327) | |
29.2 Physical Investment with Capital Adjustment Costs& (pg. 329) | |
29.3 Investment Returns (pg. 330) | |
29.4 Stock Returns and Investment Returns (pg. 331) | |
29.5 True Production-Based Asset Pricing (pg. 332) | |
29.6 Empirical Evidence (pg. 333) | |
30. The Campbell-Shiller Approximation and Vector Autoregressions (pg. 337) | |
30.1 The Campbell-Shiller Approximation (pg. 337) | |
30.2 Return and Dividend Growth Predictability& (pg. 339) | |
30.3 Vector Autoregressions (pg. 340) | |
30.4 VARs Meet Campbell-Shiller Approximations (pg. 341) | |
30.5 Examples (pg. 342) | |
31. Long-Run Risk Models (pg. 345) | |
31.1 A Stationary Model (pg. 346) | |
31.2 Interpreting the Model (pg. 346) | |
31.3 A Cointegrated Model (pg. 348) | |
31.4 Extensions (pg. 349) | |
32. Predictability: An Overview (pg. 353) | |
32.1 Predicting the Levels of Returns (pg. 354) | |
32.2 Predicting Variances: Volatility Modeling& (pg. 366) | |
32.3 Predicting Covariances, Correlations, and Higher Moments (pg. 375) | |
32.4 Predicting Cross-Sectional Differences in Returns (pg. 378) | |
32.5 Behavioral Perspective (pg. 383) | |
32.6 Concluding Remarks (pg. 384) | |
33. Characteristics versus Covariances (pg. 385) | |
33.1 The Issue (pg. 385) | |
33.2 A Quick Survey of the Evidence (pg. 386) | |
33.3 Interpretation (pg. 387) | |
34. Volatility and the Cross Section of Stock Returns (pg. 391) | |
34.1 Overview (pg. 391) | |
34.2 Time Series Evidence (pg. 393) | |
34.3 Cross-Sectional Evidence (pg. 394) | |
34.4 Explaining a Return-Idiosyncratic Volatility Relation That Is Negative (pg. 395) | |
34.5 Common Factors in Idiosyncratic Volatility (pg. 397) | |
Appendix (pg. 399) | |
A.1 Eigenvectors (x) and Eigenvalues (λ) for Square Symmetric n × n Matrices A (pg. 399) | |
A.2 Direct Sums (pg. 400) | |
A.3 Gauss Code for the Generalized Method of Moments (pg. 400) | |
A.4 Matlab Code for the Generalized Method of Moments Contributed by Davidson Heath (pg. 410) | |
References (pg. 417) | |
Index (pg. 451) |
Wayne Ferson
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