Modeling Financial Time Series with S-PLUS®

Preface

References

Contents

S and S- PLUS

1.1 Introduction

1.2 S Objects

1.3 Modeling Functions in S+ FinMetrics

1.4 S- PLUS Resources

1.5 References

Time Series Specification, Manipulation, and Visualization in S- PLUS

2.1 Introduction

2.2 The Specification of "timeSeries” Objects in S- PLUS

2.3 Time Series Manipulation in S- PLUS

2.4 Visualizing Time Series in S- PLUS

2.5 References

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

3.6 References

Unit Root Tests

4.1 Introduction

4.2 Testing for Nonstationarity and Stationarity

4.3 Autoregressive Unit Root Tests

4.4 Stationarity Tests

4.5 Some Problems with Unit Root Tests

4.6 Efficient Unit Root Tests

4.7 References

Modeling Extreme Values

5.1 Introduction

5.2 Modeling Maxima and Worst Cases

5.3 Modeling Extremes Over High Thresholds

5.4 Hill’s Non-parametric Estimator of Tail Index

5.5 References

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 Estimation

6.6 Recursive Least Squares Estimation

6.7 References

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

7.9 Conclusion

7.10 References

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 SEMIFAR Models

8.6 Long Memory GARCH Models

8.7 Prediction from Long Memory Models

8.8 References

Rolling Analysis of Time Series

9.1 Introduction

9.2 Rolling Descriptive Statistics

9.3 Technical Analysis Indicators

9.4 Rolling Regression

9.5 Rolling Analysis of General Models Using the S+ FinMetrics Function roll

9.6 References

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

10.5 References

Vector Autoregressive Models for Multivariate Time Series

11.1 Introduction

11.2 The Stationary Vector Autoregression Model

11.3 Forecasting

11.4 Structural Analysis

11.5 An Extended Example

11.6 Bayesian Vector Autoregression

11.7 References

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

12.6 Appendix: Maximum Likelihood Estimation of a Cointegrated VECM

12.7 References

Multivariate GARCH Modeling

13.1 Introduction

13.2 Exponentially Weighted Covariance Estimate

13.3 Diagonal VEC Model

13.4 Multivariate GARCH Modeling in S+ FinMetrics

13.5 Multivariate GARCH Model Extensions

13.6 Multivariate GARCH Prediction

13.7 Custom Estimation of GARCH Models

13.8 Multivariate GARCH Model Simulation

13.9 References

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

14.6 References

Factor Models for Asset Returns

15.1 Introduction

15.2 Factor Model Specification

15.3 Macroeconomic Factor Models for Returns

15.4 Fundamental Factor Model

15.5 Statistical Factor Models for Returns

15.6 References

Term Structure of Interest Rates

16.1 Introduction

16.2 Discount, Spot and Forward Rates

16.3 Quadratic and Cubic Spline Interpolation

16.4 Smoothing Spline Interpolation

16.5 Nelson-Siegel Function

16.6 Conclusion

16.7 References

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 Controlling Robust Fitting of REGARIMA Models

17.6 Algorithms of Filtered Filtered -Estimation

17.7 References

Nonlinear Time Series Models

18.1 Introduction

18.2 BDS Test for Nonlinearity

18.3 Threshold Autoregressive Models

18.4 Smooth Transition Autoregressive Models

18.5 Markov Switching State Space Models

18.6 An Extended Example: Markov Switching Coincident Index

18.7 References

Copulas

19.1 Introduction

19.2 Motivating Example

19.3 Definitions and Basic Properties of Copulas

19.4 Parametric Copula Classes and Families

19.5 Fitting Copulas to Data

19.6 Risk Management Using Copulas

19.7 References

Continuous-Time Models for Financial Time Series

20.1 Introduction

20.2 SDEs: Background

20.3 Approximating Solutions to SDEs

20.4 S+ FinMetrics Functions for Solving SDEs

20.5 References

Generalized Method of Moments

21.1 Introduction

21.2 Single Equation Linear GMM

21.3 Estimation of S

21.4 GMM Estimation Using the S+ FinMetrics Function GMM

21.5 Hypothesis Testing for Linear Models

21.6 Nonlinear GMM

21.7 Examples of Nonlinear Models

21.8 References

Seminonparametric Conditional Density Models

22.1 Introduction

22.2 Overview of SNP Methodology

22.3 Estimating SNP Models in S+ FinMetrics

22.4 SNP Model Selection

22.5 SNP Model Diagnostics

22.6 Prediction from an SNP Model

22.7 Data Transformations

22.8 Examples

22.9 References

Efficient Method of Moments

23.1 Introduction

23.2 An Overview of the EMM Methodology

23.3 EMM Estimation in S+ FinMetrics

23.4 Examples

23.5 References

Index