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| Preface | |
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| Financial Time Series and Their Characteristics | |
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| Asset Returns | |
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| Distributional Properties of Returns | |
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| Processes Considered | |
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| Linear Time Series Analysis and Its Applications | |
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| Stationarity | |
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| Correlation and Autocorrelation Function | |
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| White Noise and Linear Time Series | |
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| Simple Autoregressive Models | |
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| Simple Moving-Average Models | |
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| Simple ARMA Models | |
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| Unit-Root Nonstationarity | |
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| Seasonal Models | |
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| Regression Models with Time Series Errors | |
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| Long-Memory Models | |
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| Some SCA Commands | |
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| Conditional Heteroscedastic Models | |
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| Characteristics of Volatility | |
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| Structure of a Model | |
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| The ARCH Model | |
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| The GARCH Model | |
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| The Integrated GARCH Model | |
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| The GARCH-M Model | |
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| The Exponential GARCH Model | |
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| The CHARMA Model | |
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| Random Coefficient Autoregressive Models | |
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| The Stochastic Volatility Model | |
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| The Long-Memory Stochastic Volatility Model | |
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| An Alternative Approach | |
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| Application | |
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| Kurtosis of GARCH Models | |
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| Some RATS Programs for Estimating Volatility Models | |
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| Nonlinear Models and Their Applications | |
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| Nonlinear Models | |
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| Nonlinearity Tests | |
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| Modeling | |
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| Forecasting | |
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| Application | |
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| Some RATS Programs for Nonlinear Volatility Models | |
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| S-Plus Commands for Neural Network | |
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| High-Frequency Data Analysis and Market Microstructure | |
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| Nonsynchronous Trading | |
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| Bid-Ask Spread | |
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| Empirical Characteristics of Transactions Data | |
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| Models for Price Changes | |
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| Duration Models | |
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| Nonlinear Duration Models | |
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| Bivariate Models for Price Change and Duration | |
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| Review of Some Probability Distributions | |
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| Hazard Function | |
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| Some RATS Programs for Duration Models | |
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| Continuous-Time Models and Their Applications | |
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| Options | |
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| Some Continuous-Time Stochastic Processes | |
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| Ito's Lemma | |
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| Distributions of Stock Prices and Log Returns | |
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| Derivation of Black-Scholes Differential Equation | |
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| Black-Scholes Pricing Formulas | |
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| An Extension of Ito's Lemma | |
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| Stochastic Integral | |
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| Jump Diffusion Models | |
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| Estimation of Continuous-Time Models | |
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| Integration of Black-Scholes Formula | |
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| Approximation to Standard Normal Probability | |
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| Extreme Values, Quantile Estimation, and Value at Risk | |
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| Value at Risk | |
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| RiskMetrics | |
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| An Econometric Approach to VaR Calculation | |
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| Quantile Estimation | |
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| Extreme Value Theory | |
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| An Extreme Value Approach to VaR | |
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| A New Approach Based on the Extreme Value Theory | |
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| Multivariate Time Series Analysis and Its Applications | |
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| Weak Stationarity and Cross-Correlation Matrixes | |
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| Vector Autoregressive Models | |
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| Vector Moving-Average Models | |
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| Vector ARMA Models | |
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| Unit-Root Nonstationarity and Co-Integration | |
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| Threshold Co-Integration and Arbitrage | |
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| Principal Component Analysis | |
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| Factor Analysis | |
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| Review of Vectors and Matrixes | |
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| Multivariate Normal Distributions | |
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| Multivariate Volatility Models and Their Applications | |
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| Reparameterization | |
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| GARCH Models for Bivariate Returns | |
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| Higher Dimensional Volatility Models | |
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| Factor-Volatility Models | |
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| Application | |
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| Multivariate t Distribution | |
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| Some Remarks on Estimation | |
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| Markov Chain Monte Carlo Methods with Applications | |
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| Markov Chain Simulation | |
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| Gibbs Sampling | |
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| Bayesian Inference | |
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| Alternative Algorithms | |
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| Linear Regression with Time-Series Errors | |
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| Missing Values and Outliers | |
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| Stochastic Volatility Models | |
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| Markov Switching Models | |
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| Forecasting | |
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| Other Applications | |
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| Index | |