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• Cover
• Series Page
• Title Page
• Copyright
• Dedication
• Preface
• Preface to the Second Edition
• Preface to the First Edition
• Chapter 1: Financial Time Series and Their Characteristics
• 1.1 Asset Returns
• 1.2 Distributional Properties of Returns
• 1.3 Processes Considered
• Appendix: R Packages
• Chapter 2: Linear Time Series Analysis and Its Applications
• 2.1 Stationarity
• 2.2 Correlation and Autocorrelation Function
• 2.3 White Noise and Linear Time Series
• 2.4 Simple AR Models
• 2.5 Simple MA Models
• 2.6 Simple ARMA Models
• 2.7 Unit-Root Nonstationarity
• 2.8 Seasonal Models
• 2.9 Regression Models with Time Series Errors
• 2.10 Consistent Covariance Matrix Estimation
• 2.11 Long-Memory Models
• Appendix: Some SCA Commands
• Chapter 3: Conditional Heteroscedastic Models
• 3.1 Characteristics of Volatility
• 3.2 Structure of a Model
• 3.3 Model Building
• 3.4 The ARCH Model
• 3.5 The GARCH Model
• 3.6 The Integrated GARCH Model
• 3.7 The GARCH-M Model
• 3.8 The Exponential GARCH Model
• 3.9 The Threshold GARCH Model
• 3.10 The CHARMA Model
• 3.11 Random Coefficient Autoregressive Models
• 3.12 Stochastic Volatility Model
• 3.13 Long-Memory Stochastic Volatility Model
• 3.14 Application
• 3.15 Alternative Approaches
• 3.16 Kurtosis of GARCH Models
• Appendix: Some RATS Programs for Estimating Volatility Models
• Chapter 4: Nonlinear Models and Their Applications
• 4.1 Nonlinear Models
• 4.2 Nonlinearity Tests
• 4.3 Modeling
• 4.4 Forecasting
• 4.5 Application
• Appendix A: Some RATS Programs for Nonlinear Volatility Models
• Appendix B: R and S-Plus Commands for Neural Network
• Chapter 5: High-Frequency Data Analysis and Market Microstructure
• 5.1 Nonsynchronous Trading
• 5.2 Bid–Ask Spread
• 5.3 Empirical Characteristics of Transactions Data
• 5.4 Models for Price Changes
• 5.5 Duration Models
• 5.6 Nonlinear Duration Models
• 5.7 Bivariate Models for Price Change and Duration
• 5.8 Application
• Appendix A: Review of Some Probability Distributions
• Appendix B: Hazard Function
• Appendix C: Some RATS Programs for Duration Models
• Chapter 6: Continuous-Time Models and Their Applications
• 6.1 Options
• 6.2 Some Continuous-Time Stochastic Processes
• 6.3 Ito’s Lemma
• 6.4 Distributions of Stock Prices and Log Returns
• 6.5 Derivation of Black–Scholes Differential Equation
• 6.6 Black–Scholes Pricing Formulas
• 6.7 Extension of Ito’s Lemma
• 6.8 Stochastic Integral
• 6.9 Jump Diffusion Models
• 6.10 Estimation of Continuous-Time Models
• Appendix A: Integration of Black–Scholes Formula
• Appendix B: Approximation to Standard Normal Probability
• Chapter 7: Extreme Values, Quantiles, and Value at Risk
• 7.1 Value at Risk
• 7.2 RiskMetrics
• 7.3 Econometric Approach to VaR Calculation
• 7.4 Quantile Estimation
• 7.5 Extreme Value Theory
• 7.6 Extreme Value Approach to VaR
• 7.7 New Approach Based on the Extreme Value Theory
• 7.8 The Extremal Index
• Chapter 8: Multivariate Time Series Analysis and Its Applications
• 8.1 Weak Stationarity and Cross-Correlation Matrices
• 8.2 Vector Autoregressive Models
• 8.3 Vector Moving-Average Models
• 8.4 Vector ARMA Models
• 8.5 Unit-Root Nonstationarity and Cointegration
• 8.6 Cointegrated VAR Models
• 8.7 Threshold Cointegration and Arbitrage
• 8.8 Pairs Trading
• Appendix A: Review of Vectors and Matrices
• Appendix B: Multivariate Normal Distributions
• Appendix C: Some SCA Commands
• Chapter 9: Principal Component Analysis and Factor Models
• 9.1 A Factor Model
• 9.2 Macroeconometric Factor Models
• 9.3 Fundamental Factor Models
• 9.4 Principal Component Analysis
• 9.5 Statistical Factor Analysis
• 9.6 Asymptotic Principal Component Analysis
• Chapter 10: Multivariate Volatility Models and Their Applications
• 10.1 Exponentially Weighted Estimate
• 10.2 Some Multivariate GARCH Models
• 10.3 Reparameterization
• 10.4 GARCH Models for Bivariate Returns
• 10.5 Higher Dimensional Volatility Models
• 10.6 Factor–Volatility Models
• 10.7 Application
• 10.8 Multivariate t Distribution
• 10.9 Appendix: Some Remarks on Estimation
• Chapter 11: State-Space Models and Kalman Filter
• 11.1 Local Trend Model
• 11.2 Linear State-Space Models
• 11.3 Model Transformation
• 11.4 Kalman Filter and Smoothing
• 11.5 Missing Values
• 11.6 Forecasting
• 11.7 Application
• Chapter 12: Markov Chain Monte Carlo Methods with Applications
• 12.1 Markov Chain Simulation
• 12.2 Gibbs Sampling
• 12.3 Bayesian Inference
• 12.4 Alternative Algorithms
• 12.5 Linear Regression with Time Series Errors
• 12.6 Missing Values and Outliers
• 12.7 Stochastic Volatility Models
• 12.8 New Approach to SV Estimation
• 12.9 Markov Switching Models
• 12.10 Forecasting
• 12.11 Other Applications
• Index
• both