Campbell, John Y., Lo, Andrew W., Mackinlay, A. Craig 　著

内容

目次

List of Figures xiii List of Tables xv Preface xix 1 Introduction 31.1 Organization of the Book 4 1.2 Useful Background 6 1.2.1 MathematicsBackground 6 1.2.2 Probability and Statistics Background 6 1.2.3 FinanceTheory Background 7 1.3 Notation 8 1.4 Prices, Returns, and Compounding 91.4.1 Definitions and Conventions 9 1.4.2 The Marginal, Conditional, andJoint Distribution of Returns 13 1.5 Market Efficiency 20 1.5.1 EfficientMarkets and the Law of Iterated Expectations 22 1.5.2 Is Market EfficiencyTestable? 24 2 The Predictability of Asset Returns 27 2.1 The Random WalkHypotheses 28 2.1.1 The Random Walk 1: IID Increments 31 2.1.2 The RandomWalk 2: Independent Increments 32 2.1.3 The Random Walk 3: UncorrelatedIncrements 33 2.2 Tests of Random Walk 1: IID Increments 33 2.2.1Traditional Statistical Tests 33 2.2.2 Sequences and Reversals, and Runs 342.3 Tests of Random Walk 2: Independent Increments 41 2.3.1 Filter Rules42 2.3.2 Technical Analysis 43 2.4 Tests of Random Walk 3: UncorrelatedIncrements 44 2.4.1 Autocorrelation Coefficients 44 2.4.2 PortmanteauStatistics 47 2.4.3 Variance Ratios 48 2.5 Long-Horizon Returns 552.5.1 Problems with Long-Horizon Inferences 57 2.6 Tests For Long-RangeDependence 59 2.6.1 Examples of Long-Range Dependence 59 2.6.2 TheHurst-Mandelbrot Rescaled Range Statistic 62 2.7 Unit Root Tests 64 2.8Recent Empirical Evidence 65 2.8.1 Autocorrelations 66 2.8.2 VarianceRatios 68 2.8.3 Cross-Autocorrelations and Lead-Lag Relations 74 2.8.4Tests Using Long-Horizon Returns 78 2.9 Conclusion 80 3 MarketMicrostructure 83 3.1 Nonsynchronous Trading 84 3.1.1 A Model ofNonsynchronous Trading 85 3.1.2 Extensions and Generalizations 98 3.2 TheBid-Ask Spread 99 3.2.1 Bid-Ask Bounce 101 3.2.2 Components of theBid-Ask Spread 103 3.3 Modeling Transactions Data 107 3.3.1 Motivation108 3.3.2 Rounding and Barrier Models 114 3.3.3 The Ordered Probit Model122 3.4 Recent Empirical Findings 128 3.4.1 Nonsynchronous Trading 1283.4.2 Estimating the Effective Bid-Ask Spread 134 3.4.3 Transactions Data136 3.5 Conclusion 144 5 The Capital Asset Pricing Model 181 5.1 Reviewof the CAPM 181 5.2 Results from Efficient-Set Mathematics 184 5.3Statistical Framework for Estimation and Testing 188 5.3.1 Sharpe-LintnerVersion 189 5.3.2 Black Version 196 5.4 Size of Tests 203 5.5 Power ofTests 204 5.6 Nonnormal and Non-IID Returns 208 5.7 Implementation ofTests 211 5.7.1 Summary of Empirical Evidence 211 5.7.2 IllustrativeImplementation 212 5.7.3 Unobservability of the Market Portfolio 213 5.8Cross-Sectional Regressions 215 5.9 Conclusion 217 6 Multifactor PricingModels 219 6.1 Theoretical Background 219 6.2 Estimation and Testing 2226.2.1 Portfolios as Factors with a Riskfree Asset 223 6.2.2 Portfolios asFactors without a Riskfree Asset 224 6.2.3 Macroeconomic Variables asFactors 226 6.2.4 Factor Portfolios Spanning the Mean-Variance\protect\\Frontier 228 6.3 Estimation of Risk Premia and Expected Returns 231 6.4Selection of Factors 233 6.4.1 Statistical Approaches 233 6.4.2 Number ofFactors 238 6.4.3 Theoretical Approaches 239 6.5 Empirical Results 2406.6 Interpreting Deviations from Exact Factor Pricing 242 6.6.1 ExactFactor Pricing Models, Mean-Variance Analysis, and the Optimal OrthogonalPortfolio 243 6.6.2 Squared Sharpe Ratios 245 6.6.3 Implications forSeparating Alternative Theories 246 6.7 Conclusion 251 7 Present-ValueRelations 253 7.1 The Relation between Prices, Dividends, and Returns 2547.1.1 The Linear Present-Value Relation with Constant Expected Returns 2557.1.2 Rational Bubbles 258 7.1.3 An Approximate Present-Value Relation withTime-Varying Expected Returns 260 7.1.4 Prices and Returns in a SimpleExample 264 7.2 Present-Value Relations and US Stock Price Behavior 2677.2.1 Long-Horizon Regressions 267 7.2.2 Volatility Tests 275 7.2.3Vector Autoregressive Methods 279 7.3 Conclusion 286 8 IntertemporalEquilibrium Models 291 8.1 The Stochastic Discount Factor 293 8.1.1Volatility Bounds 296 8.2 Consumption-Based Asset Pricing with PowerUtility 304 8.2.1 Power Utility in a Lognormal Model 306 8.2.2 PowerUtility and Generalized Method of\protect\\ Moments 314 8.3 MarketFrictions 314 8.3.1 Market Frictions and Hansen-Jagannathan\protect\\Bounds 315 8.3.2 Market Frictions and Aggregate Consumption\protect\\ Data316 8.4 More General Utility Functions 326 8.4.1 Habit Formation 3268.4.2 Psychological Models of Preferences 332 8.5 Conclusion 334 9Derivative Pricing Models 339 9.1 Brownian Motion 341 9.1.1 ConstructingBrownian Motion 341 9.1.2 Stochastic Differential Equations 346 9.2 ABrief Review of Derivative Pricing Methods 349 9.2.1 The Black-Scholes andMerton Approach 350 9.2.2 The Martingale Approach 354 9.3 ImplementingParametric Option Pricing Models 355 9.3.1 Parameter Estimation of AssetPrice Dynamics 356 9.3.2 Estimating $\sigma $ in the Black-Scholes Model361 9.3.3 Quantifying the Precision of Option Price Estimators 367 9.3.4The Effects of Asset Return Predictability 369 9.3.5 Implied VolatilityEstimators 377 9.3.6 Stochastic Volatility Models 379 9.4 PricingPath-Dependent Derivatives Via Monte Carlo Simulation 382 9.4.1 DiscreteVersus Continuous Time 383 9.4.2 How Many Simulations to Perform 3849.4.3 Comparisons with a Closed-Form Solution 384 9.4.4 ComputationalEfficiency 386 9.4.5 Extensions and Limitations 390 9.5 Conclusion 39110 Fixed-Income Securities 395 10.1 Basic Concepts 396 10.1.1 DiscountBonds 397 10.1.2 Coupon Bonds 401 10.1.3 Estimating the Zero-Coupon TermStructure 409 10.2 Interpreting the Term Structure of Interest Rates 41310.2.1 The Expectations Hypothesis 413 10.2.2 Yield Spreads and InterestRate Forecasts 418 10.3 Conclusion 423 11 Term-Structure Models 427 11.1Affine-Yield Models 428 11.1.1 A Homoskedastic Single-Factor Model 42911.1.2 A Square-Root Single-Factor Model 435 11.1.3 A Two-Factor Model 43811.1.4 Beyond Affine-Yield Models 441 11.2 Fitting Term-Structure Modelsto the Data 442 11.2.1 Real Bonds, Nominal Bonds, and Inflation 44211.2.2 Empirical Evidence on Affine-Yield Models 445 11.3 PricingFixed-Income Derivative Securities 455 11.3.1 Fitting the Current TermStructure Exactly 456 11.3.2 Forwards and Futures 458 11.3.3 OptionPricing in a Term-Structure Model 461 11.4 Conclusion 464 12Nonlinearities in Financial Data 467 12.1 Nonlinear Structure in UnivariateTime Series 468 12.1.1 Some Parametric Models 470 12.1.2 Univariate Testsfor Nonlinear Structure 475 12.2 Models of Changing Volatility 479 12.2.1Univariate Models 481 12.2.2 Multivariate Models 490 12.2.3 Links betweenFirst and Second Moments 494 12.3 Nonparametric Estimation 498 12.3.1Kernel Regression 500 12.3.2 Optimal Bandwidth Selection 502 12.3.3Average Derivative Estimators 504 12.3.4 Application: EstimatingState-Price Densities 507 12.4 Artificial Neural Networks 512 12.4.1Multilayer Perceptrons 512 12.4.2 Radial Basis Functions 516 12.4.3Projection Pursuit Regression 518 12.4.4 Limitations of Learning Networks518 12.4.5 Application: Learning the Black-Scholes Formula 519 12.5Overfitting and Data-Snooping 523 12.6 Conclusion 524 Appendix 527 A.1Linear Instrumental Variables 527 A.2 Generalized Method of Moments 532A.3 Serially Correlated and Heteroskedastic Errors 534 A.4 GMM and MaximumLikelihood 536 References 541 Author Index 587 Subject Index 597