To Teresa and my father, and in memory of my mother
- 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
- 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
- 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
- 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
- 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
- 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
-
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