<p>Title: Analysis Financial Time Series<br/>Author: RUEY S. TSAY<br/>Publisher: A JOHN WILEY & SONS, INC., PUBLICATION<br/>ISBN: ISBN-13 978-0-471-69074-0<br/>Year: 2005, Format: PDF, Page: 638</p><p>Contents:<br/>1. Financial Time Series and Their Characteristics 1<br/>1.1 Asset Returns, 2<br/>1.2 Distributional Properties of Returns, 7<br/>1.2.1 Review of Statistical Distributions and Their Moments, 7<br/>1.2.2 Distributions of Returns, 13<br/>1.2.3 Multivariate Returns, 16<br/>1.2.4 Likelihood Function of Returns, 17<br/>1.2.5 Empirical Properties of Returns, 17<br/>1.3 Processes Considered, 20<br/> Exercises, 22<br/> References, 23<br/>2. Linear Time Series Analysis and Its Applications 24<br/>2.1 Stationarity, 25<br/>2.2 Correlation and Autocorrelation Function, 25<br/>2.3 White Noise and Linear Time Series, 31<br/>2.4 Simple Autoregressive Models, 32<br/>2.4.1 Properties of AR Models, 33<br/>2.4.2 Identifying AR Models in Practice, 40<br/>2.4.3 Goodness of Fit, 46<br/>2.4.4 Forecasting, 47<br/>2.5 Simple Moving-Average Models, 50<br/>2.5.1 Properties of MA Models, 51<br/>2.5.2 Identifying MA Order, 52<br/>2.5.3 Estimation, 53<br/>2.5.4 Forecasting Using MA Models, 54<br/>2.6 Simple ARMA Models, 56<br/>2.6.1 Properties of ARMA(1,1) Models, 57<br/>2.6.2 General ARMA Models, 58<br/>2.6.3 Identifying ARMA Models, 59<br/>2.6.4 Forecasting Using an ARMA Model, 61<br/>2.6.5 Three Model Representations for an ARMA Model, 62<br/>2.7 Unit-Root Nonstationarity, 64<br/>2.7.1 Random Walk, 64<br/>2.7.2 Random Walk with Drift, 65<br/>2.7.3 Trend-Stationary Time Series, 67<br/>2.7.4 General Unit-Root Nonstationary Models, 67<br/>2.7.5 Unit-Root Test, 68<br/>2.8 Seasonal Models, 72<br/>2.8.1 Seasonal Differencing, 73<br/>2.8.2 Multiplicative Seasonal Models, 75<br/>2.9 Regression Models with Time Series Errors, 80<br/>2.10 Consistent Covariance Matrix Estimation, 86<br/>2.11 Long-Memory Models, 89<br/> Appendix: Some SCA Commands, 91<br/> Exercises, 93<br/> References, 96<br/>3. Conditional Heteroscedastic Models 97<br/>3.1 Characteristics of Volatility, 98<br/>3.2 Structure of a Model, 99<br/>3.3 Model Building, 101<br/>3.3.1 Testing for ARCH Effect, 101<br/>3.4 The ARCH Model, 102<br/>3.4.1 Properties of ARCH Models, 104<br/>3.4.2 Weaknesses of ARCH Models, 106<br/>3.4.3 Building an ARCH Model, 106<br/>3.4.4 Some Examples, 109<br/>3.5 The GARCH Model, 113<br/>3.5.1 An Illustrative Example, 116<br/>3.5.2 Forecasting Evaluation, 121<br/>3.5.3 A Two-Pass Estimation Method, 121<br/>3.6 The Integrated GARCH Model, 122<br/>3.7 The GARCH-M Model, 123<br/>3.8 The Exponential GARCH Model, 124<br/>3.8.1 An Alternative Model Form, 125<br/>3.8.2 An Illustrative Example, 126<br/>3.8.3 Second Example, 126<br/>3.8.4 Forecasting Using an EGARCH Model, 128<br/>3.9 The Threshold GARCH Model, 130<br/>3.10 The CHARMA Model, 131<br/>3.10.1 Effects of Explanatory Variables, 133<br/>3.11 Random Coefficient Autoregressive Models, 133<br/>3.12 The Stochastic Volatility Model, 134<br/>3.13 The Long-Memory Stochastic Volatility Model, 134<br/>3.14 Application, 136<br/>3.15 Alternative Approaches, 140<br/>3.15.1 Use of High-Frequency Data, 140<br/>3.15.2 Use of Daily Open, High, Low, and Close Prices, 143<br/>3.16 Kurtosis of GARCH Models, 145<br/> Appendix: Some RATS Programs for Estimating Volatility Models, 147<br/> Exercises, 148<br/> References, 151<br/>4. Nonlinear Models and Their Applications 154<br/>4.5 Application, 194<br/> Appendix A: Some RATS Programs for Nonlinear Volatility<br/> Models, 199<br/> Appendix B: S-Plus Commands for Neural Network, 200<br/> Exercises, 200<br/> References, 202<br/>5. High-Frequency Data Analysis and Market Microstructure 206<br/> Appendix A: Review of Some Probability Distributions, 242<br/> Appendix B: Hazard Function, 245<br/> Appendix C: Some RATS Programs for Duration Models, 246<br/> Exercises, 248<br/> References, 250<br/>6. Continuous-Time Models and Their Applications 251<br/> Appendix A: Integration of Black–Scholes Formula, 282<br/> Appendix B: Approximation to Standard Normal<br/> Probability, 284<br/> Exercises, 284<br/>7. Extreme Values, Quantile Estimation, and Value at Risk 287<br/> Exercises, 335<br/>8. Multivariate Time Series Analysis and Its Applications 339<br/> Appendix A: Review of Vectors and Matrices, 395<br/> Appendix B: Multivariate Normal Distributions, 399<br/> Appendix C: Some SCA Commands, 400<br/> Exercises, 401<br/> References, 402<br/>9. Principal Component Analysis and Factor Models 405<br/> Exercises, 440<br/> References, 441<br/>10. Multivariate Volatility Models and Their Applications 443<br/> Appendix: Some Remarks on Estimation, 483<br/> Exercises, 488<br/> References, 489<br/>11. State-Space Models and Kalman Filter 490<br/> Exercises, 540<br/> References, 541<br/>12. Markov Chain Monte Carlo Methods with Applications 543<br/> Exercises, 597<br/> References, 598<br/></p>
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