Chapter 1: Financial Time Series and Their Characteristics
1.2 Distributional Properties of Returns
Chapter 2: Linear Time Series Analysis and Its Applications
2.2 Correlation and Autocorrelation Function
2.3 White Noise and Linear Time Series
2.9 Regression Models with Time Series Errors
2.10 Consistent Covariance Matrix Estimation
Chapter 3: Conditional Heteroscedastic Models
3.1 Characteristics of Volatility
3.6 The Integrated GARCH Model
3.8 The Exponential GARCH Model
3.11 Random Coefficient Autoregressive Models
3.12 Stochastic Volatility Model
3.13 Long-Memory Stochastic Volatility Model
Appendix: Some RATS Programs for Estimating Volatility Models
Chapter 4: Nonlinear Models and Their Applications
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
5.3 Empirical Characteristics of Transactions Data
5.7 Bivariate Models for Price Change and Duration
Appendix A: Review of Some Probability Distributions
Appendix C: Some RATS Programs for Duration Models
Chapter 6: Continuous-Time Models and Their Applications
6.2 Some Continuous-Time Stochastic Processes
6.4 Distributions of Stock Prices and Log Returns
6.5 Derivation of Black–Scholes Differential Equation
6.6 Black–Scholes Pricing Formulas
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
7.3 Econometric Approach to VaR Calculation
7.6 Extreme Value Approach to VaR
7.7 New Approach Based on the Extreme Value Theory
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
8.5 Unit-Root Nonstationarity and Cointegration
8.7 Threshold Cointegration and Arbitrage
Appendix A: Review of Vectors and Matrices
Appendix B: Multivariate Normal Distributions
Chapter 9: Principal Component Analysis and Factor Models
9.2 Macroeconometric 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
10.4 GARCH Models for Bivariate Returns
10.5 Higher Dimensional Volatility Models
10.8 Multivariate t Distribution
10.9 Appendix: Some Remarks on Estimation
Chapter 11: State-Space Models and Kalman Filter
11.2 Linear State-Space Models
11.4 Kalman Filter and Smoothing
Chapter 12: Markov Chain Monte Carlo Methods with Applications
12.5 Linear Regression with Time Series Errors
12.6 Missing Values and Outliers
12.7 Stochastic Volatility Models