Winner of The Best of 2005 Book Awards, Riskbook.com

This book shows how current and recent market prices convey information about the probability distributions that govern future prices. Moving beyond purely theoretical models, Stephen Taylor applies methods supported by empirical research of equity and foreign exchange markets to show how daily and more frequent asset prices, and the prices of option contracts, can be used to construct and assess predictions about future prices, their volatility, and their probability distributions.

Stephen Taylor

provides a comprehensive introduction to the dynamic behavior of asset prices, relying on finance theory and statistical evidence. He uses stochastic processes to define mathematical models for price dynamics, but with less mathematics than in alternative texts. The key topics covered include random walk tests, trading rules, ARCH models, stochastic volatility models, high-frequency datasets, and the information that option prices imply about volatility and distributions.

Asset Price Dynamics, Volatility, and Prediction is ideal for students of economics, finance, and mathematics who are studying financial econometrics, and will enable researchers to identify and apply appropriate models and methods. It will likewise be a valuable resource for quantitative analysts, fund managers, risk managers, and investors who seek realistic expectations about future asset prices and the risks to which they are exposed.

Stephen J. Taylor is Professor of Finance at Lancaster University, England. He is the author of Modelling Financial Time Series and many influential articles about applications of financial econometrics.

Review:

"This book provides thorough, well-presented and concise coverage of asset price dynamics and manages to combine new developments, established issues, theory and application in a practical and refreshing manner. It is well illustrated with time series graphs and tables and has a good balance between theoretical concepts and their practical applications with a mathematical treatment that is not too specialized."--Anthony F. Gyles, RSS

Endorsements:

"I enjoyed reading this book, which offers a close to unique merging of detailed and careful empirics with the finance and time series theory associated with the study of asset pricing dynamics."--Neil Shephard, University of Oxford

"This well written text nicely balances new developments in various areas of theoretical and empirical finance, and it explains in a concise way how various models and methods are related."--Philip Hans Franses, Professor of Applied Econometrics, Econometric Institute, Erasmus University, Rotterdam

Preface
1. Introduction
　1.1 Asset Price Dynamics
　1.2 Volatility
　1.3 Prediction
　1.4 Information
　1.5 Contents
　1.6 Software
　1.7 Web Resources
PART I: Foundations
　2. Prices and Returns
　　2.1 Introduction
　　2.2 Two Examples of Price Series
　　2.3 Data-Collection Issues
　　2.4 Two Returns Series
　　2.5 Definitions of Returns
　　2.6 Further Examples of Time Series of Returns
　3. Stochastic Processes: Definitions and Examples
　　3.1 Introduction
　　3.2 Random Variables
　　3.3 Stationary Stochastic Processes
　　3.4 Uncorrelated Processes
　　3.5 ARMA Processes
　　3.6 Examples of ARMA 1 1 Specifications
　　3.7 ARIMA Processes
　　3.8 ARFIMA Processes
　　3.9 Linear Stochastic Processes
　　3.10 Continuous-Time Stochastic Processes
　　3.11 Notation for Random Variables and Observations
　4. Stylized Facts for Financial Returns
　　4.1 Introduction
　　4.2 Summary Statistics
　　4.3 Average Returns and Risk Premia
　　4.4 Standard Deviations
　　4.5 Calendar Effects
　　4.6 Skewness and Kurtosis
　　4.7 The Shape of the Returns Distribution
　　4.8 Probability Distributions for Returns
　　4.9 Autocorrelations of Returns
　　4.10 Autocorrelations of Transformed Returns
　　4.11 Nonlinearity of the Returns Process
　　4.12 Concluding Remarks
　　4.13 Appendix: Autocorrelation Caused by Day-of-the-Week Effects
　　4.14 Appendix: Autocorrelations of a Squared Linear Process
PART II: Conditional Expected Returns
　5. The Variance-Ratio Test of the Random Walk Hypothesis
　　5.1 Introduction
　　5.2 The Random Walk Hypothesis
　　5.3 Variance-Ratio Tests
　　5.4 An Example of Variance-Ratio Calculations
　　5.5 Selected Test Results
　　5.6 Sample Autocorrelation Theory
　　5.7 Random Walk Tests Using Rescaled Returns
　　5.8 Summary
　6. Further Tests of the Random Walk Hypothesis
　　6.1 Introduction
　　6.2 Test Methodology
　　6.3 Further Autocorrelation Tests
　　6.4 Spectral Tests
　　6.5 The Runs Test
　　6.6 Rescaled Range Tests
　　6.7 The BDS Test
　　6.8 Test Results for the Random Walk Hypothesis
　　6.9 The Size and Power of Random Walk Tests
　　6.10 Sources of Minor Dependence in Returns
　　6.11 Concluding Remarks
　　6.12 Appendix: the Correlation between Test Values for Two Correlated Series
　　6.13 Appendix: Autocorrelation Induced by Rescaling Returns
　7. Trading Rules and Market Efficiency
　　7.1 Introduction
　　7.2 Four Trading Rules
　　7.3 Measures of Return Predictability
　　7.4 Evidence about Equity Return Predictability
　　7.5 Evidence about the Predictability of Currency and Other Returns
　　7.6 An Example of Calculations for the Moving-Average Rule
　　7.7 Efficient Markets: Methodological Issues
　　7.8 Breakeven Costs for Trading Rules Applied to Equities
　　7.9 Trading Rule Performance for Futures Contracts
　　7.10 The Efficiency of Currency Markets
　　7.11 Theoretical Trading Profits for Autocorrelated Return Processes
　　7.12 Concluding Remarks
PART III: Volatility Processes
　8. An Introduction to Volatility
　9. ARCH Models: Definitions and Examples
　10. ARCH Models: Selection and Likelihood Methods
　11. Stochastic Volatility Models
PART IV: High-Frequency Methods
　12. High-Frequency Data and Models
PART V: Inferences from Option Prices
　13. Continuous-Time Stochastic Processes
　14. Option Pricing Formulae
　15. Forecasting Volatility
　16. Density Prediction for Asset Prices
Symbols
References
Author Index
Subject Index