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书名：Modelling Financial Time Series with S-PLUS
作者：Eric Zivot & Jiahui Wang
Contents:
1. S and S-PLUS
1.1 Intoduction
1.2 S Objects
1.3 Modeling Functions in S+FinMetrics
1.4 S-PLUS Resources
2. Time Series Specification, Manipulation and Visualization in S-PLUS
2.1 Introduction
2.2 The Specification of "Time Series" Objects in S-PLUS
2.3 Time Series Manipulation in S-PLUS
2.4 Visualization Time Series in S-PLUS
3. Time Series Concepts
3.1 Introduction
3.2 Univariate Time Series
3.3 Univariate Nonstationary Time Series
3.4 Long Memory Time Series
3.5 Multivariate Time Series
4. Unit Root Test
4.1 Introduction
4.2 Testing for Nonstationarity and Stationarity
4.3 Autoregressive Unit Root Tests
4.4 Stationarity Tests
5. Modeling Extreme Values
5.1 Introduction
5.2 Modeling Maxima and Worst Cases
5.3 Modeling Extreme Over High Thresholds
5.4 Hill's Non-parametric Estimator of Tail Index
6. Time Series Regression Modeling
6.1 Introduction
6.2 Time Series Regression Model
6.3 Time Series Regression Using the S+FinMetrics Function OLS
6.4 Dynamic Regression
6.5 Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimate
6.6 Recursive Least Squares Estimation
7. Univariate GARCH Modeling
7.1 Introduction
7.2 The Basic ARCH Model
7.3 The GARCH Model and Its Properties
7.4 GARCH Modeling Using S+FinMetrics
7.5 GARCH Model Extensions
7.6 GARCH Model Selection and Comparison
7.7 GARCH Model Prediction
7.8 GARCH Model Simulation
8. Long Memory Time Series Modeling
8.1 Introduction
8.2 Long Memory Time Series
8.3 Statistical Tests for Long Memory
8.4 Estimation of Long Memory Parameter
8.5 Estimation of FARIMA and SEMIFA Models
8.6 Long Memory GARCH Models
8.7 Prediction from Long Memory Models
9. Rolling Analysis of Time Series
9.1 Introduction
9.2 Rolling Descriptive Statistics
9.3 Technical Analysis Indicator
9.4 Rollling Regression
10. Systems of Regression Equations
10.1 Introduction
10.2 Systems of Regression Equations
10.3 Linear Seemingly Unrelated Regressions
10.4 Nonlinear Seemingly Unrelated Regression Models
11. Vector Autoregressive Models for Multivariate Time Series
11.1 Introduction
11.2 The Stationary Vector Autoregressive Models
11.3 Forecasting
11.4 Structure Analysis
11.5 An Extended Example
11.6 Bayesian Vector Autoregression
12. Cointegration
12.1 Introduction
12.2 Spurious Regression and Cointegration
12.3 Residual-Based Tests for Cointegration
12.4 Regression-Based Estimates of Cointegrating Vectors and Error Correction Models
12.5 VAR Models and Cointegration
13. Multivariate GARCH Modeling
13.1 Introduction
13.2 Expotentially Weirhted Covariance Estimate
13.3 Diagnol VEC Model
13.4 Multivariate GARCH Modeling in FinMetrics
13.5 Multivariate GARCH Model Extensions
13.6 Multivariate GARCH Prediction
13.7 Custom Estimation of GARCH Model
13.8 Multivariate GARCH Model Simulation
14. State Space Models
14.1 Introduction
14.2 State Space Representation
14.3 Algorithms
14.4 Estimation of State Space Models
14.5 Simulation Smoothing
15. Factor Models for Asset Returns
15.1 Introduction
15.2 Factor Models Specification
15.3 Macroeconomic Factor Models for Returns
15.4 Fundalmental Factor Models
15.5 Statistical Factor Models for Returns
16. Term Structure of Interest Rates
16.1 Introduction
16.2 Discount, Spot and Forward Rates
16.3 Qudratic and Cubic Spline Interpolation
16.4 Smoothing Spline Interpolation
16.5 Nelson-Siegel Function
17. Robust Change Detection
17.1 Introduction
17.2 REGARIMA Models
17.3 Robust Fitting of REGARIMA Models
17.4 Prediction Using REGARIMA Models
17.5 Controling Robust Fitting of REGARIMA Models
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