Klappentext This graduate-level textbook is intended 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 new 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 read into their own applications. Zusammenfassung The past twenty years have seen an extraordinary growth in the use of quantitative methods in financial markets. Finance professionals now routinely use sophisticated statistical techniques in portfolio management, proprietary trading, risk management, financial consulting, and securities regulation. This graduate-level textbook is intended 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 new 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 read into their own applications. Inhaltsverzeichnis 7559180 ...

Résumé

A landmark book on quantitative methods in financial markets for graduate students and finance professionals

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.

Contenu
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 …