# %{search_type} search results

## 3 catalog results

View results as:
Number of results to display per page

### 1. Empirical dynamic asset pricing : model specification and econometric assessment[2006]

Book
xiv, 480 p. : ill ; 25 cm.
• Preface xi Acknowledgments xiii Chapter 1: Introduction 1 1.1. Model Implied Restrictions 3 1.2. Econometric Estimation Strategies 10 Part I: Econometric Methods for Analyzing DAPMs 15 Chapter 2: Model Specification and Estimation Strategies 17 2.1. Full Information about Distributions 17 2.2. No Information about the Distribution 21 2.3. Limited Information: GMM Estimators 25 2.4. Summary of Estimators 34 Chapter 3: Large-Sample Properties of Extremum Estimators 35 3.1. Basic Probability Model 35 3.2. Consistency: General Considerations 39 3.3. Consistency of Extremum Estimators 44 3.4. Asymptotic Normality of Extremum Estimators 48 3.5. Distributions of Specific Estimators 53 3.6. Relative Efficiency of Estimators 60 Chapter 4: Goodness-of-Fit and Hypothesis Testing 71 4.1. GMM Tests of Goodness-of-Fit 71 4.2. Testing Restrictions on ? 0 77 4.3. Comparing LR, Wald, and LM Tests 84 4.4. Inference for Sequential Estimators 86 4.5. Inference with Unequal-Length Samples 88 4.6. Underidentified Parameters under H 0 94 Chapter 5: Affine Processes 98 5.1. Affine Processes: Overview 100 5.2. Continuous-Time Affine Processes 101 5.3. Discrete-Time Affine Processes 108 5.4. Transforms for Affine Processes 114 5.5. GMM Estimation of Affine Processes 117 5.6. ML Estimation of Affine Processes 118 5.7. Characteristic Function-Based Estimators 124 Chapter 6: Simulation-Based Estimators of DAPMs 130 6.1. Introduction 130 6.2. SME: The Estimation Problem 132 6.3. Consistency of the SME 135 6.4. Asymptotic Normality of the SME 142 6.5. Extensions of the SME 144 6.6. Moment Selection with SME 146 6.7. Applications of SME to Diffusion Models 152 6.8. Markov Chain Monte Carlo Estimation 153 Chapter 7: Stochastic Volatility, Jumps, and Asset Returns 158 7.1. Preliminary Observations about Shape 159 7.2. Discrete-Time Models 164 7.3. Estimation of Discrete-Time Models 171 7.4. Continuous-Time Models 174 7.5. Estimation of Continuous-Time Models 179 7.6. Volatility Scaling 185 7.7. Term Structures of Conditional Skewness and Kurtosis 187 Part II: Pricing Kernels, Preferences, and DAPMs 193 Chapter 8: Pricing Kernels and DAPMs 195 8.1. Pricing Kernels 195 8.2. Marginal Rates of Substitution as q *198 8.3. No-Arbitrage and Risk-Neutral Pricing 202 Chapter 9: Linear Asset Pricing Models 211 9.1. Economic Motivations for Examining Asset Return Predictability 211 9.2. Market Microstructure Effects 214 9.3. A Digression on Unit Roots in Time Series 219 9.4. Tests for Serial Correlation in Returns 224 9.5. Evidence on Stock-Return Predictability 231 9.6. Time-Varying Expected Returns on Bonds 237 Chapter 10: Consumption-Based DAPMs 246 10.1. Empirical Challenges Facing DAPMs 247 10.2. Assessing Goodness-of-Fit 251 10.3. Time-Separable Single-Good Models 254 10.4. Models with Durable Goods 260 10.5. Habit Formation 265 10.6. Non-State-Separable Preferences 274 10.7. Other Preference-Based Models 276 10.8. Bounds on the Volatility of m nt 277 Chapter 11: Pricing Kernels and Factor Models 282 11.1. A Single-Beta Representation of Returns 283 11.2. Beta Representations of Excess Returns 285 11.3. Conditioning Down and Beta Relations 287 11.4. From Pricing Kernels to Factor Models 290 11.5. Methods for Testing Beta Models 297 11.6. Empirical Analyses of Factor Models 302 Part III: No-Arbitrage DAPMs 309 Chapter 12: Models of the Term Structure of Bond Yields 311 12.1. Key Ingredients of a DTSM 312 12.2. Affine Term Structure Models 316 12.3. Continuous-Time Affine DTSMs 317 12.4. Discrete-Time Affine DSTMs 327 12.5. Quadratic-Gaussian Models 329 12.6. NonAffine Stochastic Volatility Models 331 12.7. Bond Pricing with Jumps 332 12.8. DTSMs with Regime Shifts 334 Chapter 13: Empirical Analyses of Dynamic Term Structure Models 338 13.1. Estimation of DTSMs 338 13.2. Empirical Challenges for DTSMs 344 13.3. DTSMs of Swap and Treasury Yields 348 13.4. Factor Interpretations in Affine DTSMs 356 13.5. Macroeconomic Factors and DTSMs 359 Chapter 14: Term Structures of Corporate Bond Spreads 364 14.1. DTSMs of Defaultable Bonds 364 14.2. Parametric Reduced-Form Models 369 14.3. Parametric Structural Models 371 14.4. Empirical Studies of Corporate Bonds 373 14.5. Modeling Interest Rate Swap Spreads 383 14.6. Pricing Credit Default Swaps 384 14.7. Is Default Risk Priced? 387 Chapter 15: Equity Option Pricing Models 391 15.1. No-Arbitrage Option Pricing Models 392 15.2. Option Pricing 396 15.3. Estimation of Option Pricing Models 397 15.4. Econometric Analysis of Option Prices 401 15.5. Options and Revealed Preferences 404 15.6. Options on Individual Common Stocks 410 Chapter 16: Pricing Fixed-Income Derivatives 412 16.1. Pricing with Affine DTSMs 413 16.2. Pricing Using Forward-Rate Models 417 16.3. Risk Factors and Derivatives Pricing 425 16.4. Affine Models of Derivatives Prices 428 16.5. Forward-Rate-Based Pricing Models 429 16.6. On Model-Basing Hedging 431 16.7. Pricing Eurodollar Futures Options 433 References 435 Index 465.
• (source: Nielsen Book Data)9780691122977 20160528
Written by one of the leading experts in the field, this book focuses on the interplay between model specification, data collection, and econometric testing of dynamic asset pricing models. The first several chapters provide an in-depth treatment of the econometric methods used in analyzing financial time-series models. The remainder explores the goodness-of-fit of preference-based and no-arbitrage models of equity returns and the term structure of interest rates; equity and fixed-income derivatives prices; and the prices of defaultable securities. Singleton addresses the restrictions on the joint distributions of asset returns and other economic variables implied by dynamic asset pricing models, as well as the interplay between model formulation and the choice of econometric estimation strategy. For each pricing problem, he provides a comprehensive overview of the empirical evidence on goodness-of-fit, with tables and graphs that facilitate critical assessment of the current state of the relevant literatures. As an added feature, Singleton includes throughout the book interesting tidbits of new research. These range from empirical results (not reported elsewhere, or updated from Singleton's previous papers) to new observations about model specification and new econometric methods for testing models. Clear and comprehensive, the book will appeal to researchers at financial institutions as well as advanced students of economics and finance, mathematics, and science.
(source: Nielsen Book Data)9780691122977 20160528
FINANCE-625-01

### 2. Asset pricing[2005]

Book
xvii, 533 p. : ill ; 24 cm.
• Consumption-based model and overview
• Applying the basic model
• Contingent claims markets
• The discount factor
• Mean-variance frontier and beta representations
• Relation between discount factors, betas, and mean-variance frontiers
• Implications of existence and equivalence theorems
• Conditioning information
• Factor pricing models
• GMM in explicit discount factor models
• GMM : general formulas and applications
• Regression-based tests of linear factor models
• GMM for linear factor models in discount factor form
• Maximum likelihood
• Time-series, cross-section, and GMM/DF tests of linear factor models
• Which method?
• Option pricing
• Option pricing without perfect replication
• Term structure of interest rates
• Expected returns in the time series and cross section
• Equity premium puzzle and consumption-based models
• Appendix:
• A.1 Brownian motion
• A.2 Diffusion model
• A.3 Ito's Lemma
Winner of the prestigious Paul A. Samuelson Award for scholarly writing on lifelong financial security, John Cochrane's Asset Pricing now appears in a revised edition that unifies and brings the science of asset pricing up to date for advanced students and professionals. Cochrane traces the pricing of all assets back to a single idea - price equals expected discounted payoff - that captures the macro-economic risks underlying each security's value. By using a single, stochastic discount factor rather than a separate set of tricks for each asset class, Cochrane builds a unified account of modern asset pricing. He presents applications to stocks, bonds, and options. Each model - consumption based, CAPM, multifactor, term structure, and option pricing - is derived as a different specification of the discounted factor. The discount factor framework also leads to a state-space geometry for mean-variance frontiers and asset pricing models. It puts payoffs in different states of nature on the axes rather than mean and variance of return, leading to a new and conveniently linear geometrical representation of asset pricing ideas. Cochrane approaches empirical work with the Generalized Method of Moments, which studies sample average prices and discounted payoffs to determine whether price does equal expected discounted payoff. He translates between the discount factor, GMM, and state-space language and the beta, mean-variance, and regression language common in empirical work and earlier theory. The book also includes a review of recent empirical work on return predictability, value and other puzzles in the cross section, and equity premium puzzles and their resolution. Written to be a summary for academics and professionals as well as a textbook, this book condenses and advances recent scholarship in financial economics.
(source: Nielsen Book Data)9780691121376 20160528
• List of Figures xiii List of Tables xv Preface xix 1 Introduction 3 1.1 Organization of the Book 4 1.2 Useful Background 6 1.2.1 Mathematics Background 6 1.2.2 Probability and Statistics Background 6 1.2.3 Finance Theory Background 7 1.3 Notation 8 1.4 Prices, Returns, and Compounding 9 1.4.1 Definitions and Conventions 9 1.4.2 The Marginal, Conditional, and Joint Distribution of Returns 13 1.5 Market Efficiency 20 1.5.1 Efficient Markets and the Law of Iterated Expectations 22 1.5.2 Is Market Efficiency Testable? 24 2 The Predictability of Asset Returns 27 2.1 The Random Walk Hypotheses 28 2.1.1 The Random Walk 1: IID Increments 31 2.1.2 The Random Walk 2: Independent Increments 32 2.1.3 The Random Walk 3: Uncorrelated Increments 33 2.2 Tests of Random Walk 1: IID Increments 33 2.2.1 Traditional Statistical Tests 33 2.2.2 Sequences and Reversals, and Runs 34 2.3 Tests of Random Walk 2: Independent Increments 41 2.3.1 Filter Rules 42 2.3.2 Technical Analysis 43 2.4 Tests of Random Walk 3: Uncorrelated Increments 44 2.4.1 Autocorrelation Coefficients 44 2.4.2 Portmanteau Statistics 47 2.4.3 Variance Ratios 48 2.5 Long-Horizon Returns 55 2.5.1 Problems with Long-Horizon Inferences 57 2.6 Tests For Long-Range Dependence 59 2.6.1 Examples of Long-Range Dependence 59 2.6.2 The Hurst-Mandelbrot Rescaled Range Statistic 62 2.7 Unit Root Tests 64 2.8 Recent Empirical Evidence 65 2.8.1 Autocorrelations 66 2.8.2 Variance Ratios 68 2.8.3 Cross-Autocorrelations and Lead-Lag Relations 74 2.8.4 Tests Using Long-Horizon Returns 78 2.9 Conclusion 80 3 Market Microstructure 83 3.1 Nonsynchronous Trading 84 3.1.1 A Model of Nonsynchronous Trading 85 3.1.2 Extensions and Generalizations 98 3.2 The Bid-Ask Spread 99 3.2.1 Bid-Ask Bounce 101 3.2.2 Components of the Bid-Ask Spread 103 3.3 Modeling Transactions Data 107 3.3.1 Motivation 108 3.3.2 Rounding and Barrier Models 114 3.3.3 The Ordered Probit Model 122 3.4 Recent Empirical Findings 128 3.4.1 Nonsynchronous Trading 128 3.4.2 Estimating the Effective Bid-Ask Spread 134 3.4.3 Transactions Data 136 3.5 Conclusion 144 5 The Capital Asset Pricing Model 181 5.1 Review of the CAPM 181 5.2 Results from Efficient-Set Mathematics 184 5.3 Statistical Framework for Estimation and Testing 188 5.3.1 Sharpe-Lintner Version 189 5.3.2 Black Version 196 5.4 Size of Tests 203 5.5 Power of Tests 204 5.6 Nonnormal and Non-IID Returns 208 5.7 Implementation of Tests 211 5.7.1 Summary of Empirical Evidence 211 5.7.2 Illustrative Implementation 212 5.7.3 Unobservability of the Market Portfolio 213 5.8 Cross-Sectional Regressions 215 5.9 Conclusion 217 6 Multifactor Pricing Models 219 6.1 Theoretical Background 219 6.2 Estimation and Testing 222 6.2.1 Portfolios as Factors with a Riskfree Asset 223 6.2.2 Portfolios as Factors without a Riskfree Asset 224 6.2.3 Macroeconomic Variables as Factors 226 6.2.4 Factor Portfolios Spanning the Mean-Variance\protect\\ Frontier 228 6.3 Estimation of Risk Premia and Expected Returns 231 6.4 Selection of Factors 233 6.4.1 Statistical Approaches 233 6.4.2 Number of Factors 238 6.4.3 Theoretical Approaches 239 6.5 Empirical Results 240 6.6 Interpreting Deviations from Exact Factor Pricing 242 6.6.1 Exact Factor Pricing Models, Mean-Variance Analysis, and the Optimal Orthogonal Portfolio 243 6.6.2 Squared Sharpe Ratios 245 6.6.3 Implications for Separating Alternative Theories 246 6.7 Conclusion 251 7 Present-Value Relations 253 7.1 The Relation between Prices, Dividends, and Returns 254 7.1.1 The Linear Present-Value Relation with Constant Expected Returns 255 7.1.2 Rational Bubbles 258 7.1.3 An Approximate Present-Value Relation with Time-Varying Expected Returns 260 7.1.4 Prices and Returns in a Simple Example 264 7.2 Present-Value Relations and US Stock Price Behavior 267 7.2.1 Long-Horizon Regressions 267 7.2.2 Volatility Tests 275 7.2.3 Vector Autoregressive Methods 279 7.3 Conclusion 286 8 Intertemporal Equilibrium Models 291 8.1 The Stochastic Discount Factor 293 8.1.1 Volatility Bounds 296 8.2 Consumption-Based Asset Pricing with Power Utility 304 8.2.1 Power Utility in a Lognormal Model 306 8.2.2 Power Utility and Generalized Method of\protect\\ Moments 314 8.3 Market Frictions 314 8.3.1 Market Frictions and Hansen-Jagannathan\protect\\ Bounds 315 8.3.2 Market Frictions and Aggregate Consumption\protect\\ Data 316 8.4 More General Utility Functions 326 8.4.1 Habit Formation 326 8.4.2 Psychological Models of Preferences 332 8.5 Conclusion 334 9 Derivative Pricing Models 339 9.1 Brownian Motion 341 9.1.1 Constructing Brownian Motion 341 9.1.2 Stochastic Differential Equations 346 9.2 A Brief Review of Derivative Pricing Methods 349 9.2.1 The Black-Scholes and Merton Approach 350 9.2.2 The Martingale Approach 354 9.3 Implementing Parametric Option Pricing Models 355 9.3.1 Parameter Estimation of Asset Price Dynamics 356 9.3.2 Estimating $\sigma$ in the Black-Scholes Model 361 9.3.3 Quantifying the Precision of Option Price Estimators 367 9.3.4 The Effects of Asset Return Predictability 369 9.3.5 Implied Volatility Estimators 377 9.3.6 Stochastic Volatility Models 379 9.4 Pricing Path-Dependent Derivatives Via Monte Carlo Simulation 382 9.4.1 Discrete Versus Continuous Time 383 9.4.2 How Many Simulations to Perform 384 9.4.3 Comparisons with a Closed-Form Solution 384 9.4.4 Computational Efficiency 386 9.4.5 Extensions and Limitations 390 9.5 Conclusion 391 10 Fixed-Income Securities 395 10.1 Basic Concepts 396 10.1.1 Discount Bonds 397 10.1.2 Coupon Bonds 401 10.1.3 Estimating the Zero-Coupon Term Structure 409 10.2 Interpreting the Term Structure of Interest Rates 413 10.2.1 The Expectations Hypothesis 413 10.2.2 Yield Spreads and Interest Rate Forecasts 418 10.3 Conclusion 423 11 Term-Structure Models 427 11.1 Affine-Yield Models 428 11.1.1 A Homoskedastic Single-Factor Model 429 11.1.2 A Square-Root Single-Factor Model 435 11.1.3 A Two-Factor Model 438 11.1.4 Beyond Affine-Yield Models 441 11.2 Fitting Term-Structure Models to the Data 442 11.2.1 Real Bonds, Nominal Bonds, and Inflation 442 11.2.2 Empirical Evidence on Affine-Yield Models 445 11.3 Pricing Fixed-Income Derivative Securities 455 11.3.1 Fitting the Current Term Structure Exactly 456 11.3.2 Forwards and Futures 458 11.3.3 Option Pricing in a Term-Structure Model 461 11.4 Conclusion 464 12 Nonlinearities in Financial Data 467 12.1 Nonlinear Structure in Univariate Time Series 468 12.1.1 Some Parametric Models 470 12.1.2 Univariate Tests for Nonlinear Structure 475 12.2 Models of Changing Volatility 479 12.2.1 Univariate Models 481 12.2.2 Multivariate Models 490 12.2.3 Links between First and Second Moments 494 12.3 Nonparametric Estimation 498 12.3.1 Kernel Regression 500 12.3.2 Optimal Bandwidth Selection 502 12.3.3 Average Derivative Estimators 504 12.3.4 Application: Estimating State-Price Densities 507 12.4 Artificial Neural Networks 512 12.4.1 Multilayer Perceptrons 512 12.4.2 Radial Basis Functions 516 12.4.3 Projection Pursuit Regression 518 12.4.4 Limitations of Learning Networks 518 12.4.5 Application: Learning the Black-Scholes Formula 519 12.5 Overfitting and Data-Snooping 523 12.6 Conclusion 524 Appendix 527 A.1 Linear Instrumental Variables 527 A.2 Generalized Method of Moments 532 A.3 Serially Correlated and Heteroskedastic Errors 534 A.4 GMM and Maximum Likelihood 536 References 541 Author Index 587 Subject Index 597.