This book provides a systematic and mathematically accessible introduction to financial econometric models and their applications in modeling and predicting financial time series data. It emphasizes empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure, and high-frequency financial data. S-Plus(r) commands and illustrations are used extensively throughout the book in order to highlight accurate interpretations and graphical representations of financial data. Exercises are included in order to provide readers with more opportunities to put the models and methods into everyday practice. The tools provided in the text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data, most importantly without needless computation.
1. Financial Data and Their Properties 1 1.1 Asset Returns 2 1.2 Bond Yields and Prices 7 1.3 Implied Volatility 10 1.4 R Packages and Demonstrations 11 1.5 Examples of Financial Data 17 1.6 Distributional Properties of Returns 20 1.7 Visualization of Financial Data 27 1.8 Some Statistical Distributions 32 2. Linear Models for Financial Times Series 39 2.1 Stationarity 40 2.2 Autocorrelation 43 2.3 Linear time series 49 2.4 Simple AR models 51 2.5 Simple MA models 69 2.6 Simple ARMA Models 78 2.7 Unit-Root Nonstationarity 86 2.8 Exponential Smoothing 94 2.9 Seasonal Models 97 2.10 Regression with Correlated Errors 108 2.11 Long-Memory Models 115 2.12 Model Comparison and Averaging 118 3. Case Studies of Linear Time Series 127 3.1 Weekly Regular Gasoline Price 128 3.2 Global Temperature Anomalies 139 3.3 U.S. Monthly Unemployment Rates 156 4. Volatility models 175 4.1 Characteristics of Volatility 176 4.2 Structure of a Model 177 4.3 Model Building 180 4.4 Testing for ARCH Effect 180 4.5 The ARCH Model 184 4.6 The GARTH Model 197 4.7 The Integrated GARCH Model 209 4.8 The GARCH-M Model 211 4.9 The Exponential GARCH Model 213 4.10 The Threshold GARCH Model 219 4.11 Asymmetric Power ARCH Models 221 4.12 An Non-symmetric GARCH Model 224 4.13 The Stochastic Volatility Model 226 4.14 Long-Memory Stochastic Volatility Models 227 4.15 Alternative Approaches 229 5. Applications of Volatility Models 241 5.1 GARCH Volatility Terms Structure 242 5.2 Option Pricing and Hedging 245 5.3 Time-varying Correlations and Betas 248 5.4 Minimum Variance Portfolios 256 5.5 Prediction 260 6. High-Frequency Financial Data 271 6.1 Nonsynchronous Trading 272 6.2 Bid - Ask Spread of Trading Prices 275 6.3 Empirical Characteristics of Trading Data 278 6.4 Models for Price Changes 285 6.5 Duration Models 296 6.5 Realized Volatility 305 7. Value at Risk 325 7.1 Risk Measure and Coherence 326 7.2 Remarks on Calculating Risk Measures 334 7.3 RiskMetrics 335 7.4 An Econometric Approach 342 7.5 Quantile Estimation 349 7.6 Extreme Value Theory 354 7.7 An Extreme Value Approach to VaR 364 7.8 Peaks Over Thresholds 369 7.9 The Stationary Loss Processes 378
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