Asset price dynamics volatility and prediction 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. Intro Title Copyright Contents 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 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 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.