Modeling Financial Time Series with S-PLUS®

von: Eric Zivot, Jiahui Wang

Springer-Verlag, 2007

ISBN: 9780387323480 , 998 Seiten

2. Auflage

Format: PDF

Kopierschutz: Wasserzeichen

Windows PC,Mac OSX Apple iPad, Android Tablet PC's

Preis: 128,39 EUR

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Mehr zum Inhalt

Modeling Financial Time Series with S-PLUS®


 

Preface

5

References

10

Contents

11

S and S- PLUS

23

1.1 Introduction

23

1.2 S Objects

24

1.3 Modeling Functions in S+ FinMetrics

30

1.4 S- PLUS Resources

34

1.5 References

35

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

37

2.1 Introduction

37

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

37

2.3 Time Series Manipulation in S- PLUS

62

2.4 Visualizing Time Series in S- PLUS

70

2.5 References

77

Time Series Concepts

78

3.1 Introduction

78

3.2 Univariate Time Series

79

3.3 Univariate Nonstationary Time Series

114

3.4 Long Memory Time Series

118

3.5 Multivariate Time Series

122

3.6 References

130

Unit Root Tests

132

4.1 Introduction

132

4.2 Testing for Nonstationarity and Stationarity

133

4.3 Autoregressive Unit Root Tests

135

4.4 Stationarity Tests

150

4.5 Some Problems with Unit Root Tests

153

4.6 Efficient Unit Root Tests

153

4.7 References

159

Modeling Extreme Values

161

5.1 Introduction

161

5.2 Modeling Maxima and Worst Cases

162

5.3 Modeling Extremes Over High Thresholds

177

5.4 Hill’s Non-parametric Estimator of Tail Index

194

5.5 References

198

Time Series Regression Modeling

200

6.1 Introduction

200

6.2 Time Series Regression Model

201

6.3 Time Series Regression Using the S+ FinMetrics Function OLS

204

6.4 Dynamic Regression

220

6.5 Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation

227

6.6 Recursive Least Squares Estimation

236

6.7 References

240

Univariate GARCH Modeling

242

7.1 Introduction

242

7.2 The Basic ARCH Model

243

7.3 The GARCH Model and Its Properties

248

7.4 GARCH Modeling Using S+ FinMetrics

251

7.5 GARCH Model Extensions

259

7.6 GARCH Model Selection and Comparison

279

7.7 GARCH Model Prediction

281

7.8 GARCH Model Simulation

284

7.9 Conclusion

286

7.10 References

286

Long Memory Time Series Modeling

289

8.1 Introduction

289

8.2 Long Memory Time Series

290

8.3 Statistical Tests for Long Memory

294

8.4 Estimation of Long Memory Parameter

298

8.5 Estimation of FARIMA and SEMIFAR Models

302

8.6 Long Memory GARCH Models

314

8.7 Prediction from Long Memory Models

322

8.8 References

327

Rolling Analysis of Time Series

330

9.1 Introduction

330

9.2 Rolling Descriptive Statistics

331

9.3 Technical Analysis Indicators

354

9.4 Rolling Regression

359

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

375

9.6 References

377

Systems of Regression Equations

378

10.1 Introduction

378

10.2 Systems of Regression Equations

379

10.3 Linear Seemingly Unrelated Regressions

381

10.4 Nonlinear Seemingly Unrelated Regression Models

391

10.5 References

399

Vector Autoregressive Models for Multivariate Time Series

401

11.1 Introduction

401

11.2 The Stationary Vector Autoregression Model

402

11.3 Forecasting

414

11.4 Structural Analysis

422

11.5 An Extended Example

432

11.6 Bayesian Vector Autoregression

440

11.7 References

444

Cointegration

446

12.1 Introduction

446

12.2 Spurious Regression and Cointegration

447

12.3 Residual-Based Tests for Cointegration

459

12.4 Regression-Based Estimates of Cointegrating Vectors and Error Correction Models

465

12.5 VAR Models and Cointegration

470

12.6 Appendix: Maximum Likelihood Estimation of a Cointegrated VECM

491

12.7 References

493

Multivariate GARCH Modeling

496

13.1 Introduction

496

13.2 Exponentially Weighted Covariance Estimate

497

13.3 Diagonal VEC Model

501

13.4 Multivariate GARCH Modeling in S+ FinMetrics

502

13.5 Multivariate GARCH Model Extensions

511

13.6 Multivariate GARCH Prediction

524

13.7 Custom Estimation of GARCH Models

527

13.8 Multivariate GARCH Model Simulation

530

13.9 References

532

State Space Models

534

14.1 Introduction

534

14.2 State Space Representation

535

14.3 Algorithms

558

14.4 Estimation of State Space Models

567

14.5 Simulation Smoothing

580

14.6 References

581

Factor Models for Asset Returns

583

15.1 Introduction

583

15.2 Factor Model Specification

584

15.3 Macroeconomic Factor Models for Returns

585

15.4 Fundamental Factor Model

594

15.5 Statistical Factor Models for Returns

604

15.6 References

628

Term Structure of Interest Rates

631

16.1 Introduction

631

16.2 Discount, Spot and Forward Rates

632

16.3 Quadratic and Cubic Spline Interpolation

634

16.4 Smoothing Spline Interpolation

638

16.5 Nelson-Siegel Function

642

16.6 Conclusion

646

16.7 References

647

Robust Change Detection

649

17.1 Introduction

649

17.2 REGARIMA Models

650

17.3 Robust Fitting of REGARIMA Models

651

17.4 Prediction Using REGARIMA Models

656

17.5 Controlling Robust Fitting of REGARIMA Models

657

17.6 Algorithms of Filtered Filtered -Estimation

663

17.7 References

665

Nonlinear Time Series Models

667

18.1 Introduction

667

18.2 BDS Test for Nonlinearity

668

18.3 Threshold Autoregressive Models

676

18.4 Smooth Transition Autoregressive Models

692

18.5 Markov Switching State Space Models

701

18.6 An Extended Example: Markov Switching Coincident Index

715

18.7 References

723

Copulas

727

19.1 Introduction

727

19.2 Motivating Example

728

19.3 Definitions and Basic Properties of Copulas

736

19.4 Parametric Copula Classes and Families

743

19.5 Fitting Copulas to Data

761

19.6 Risk Management Using Copulas

768

19.7 References

771

Continuous-Time Models for Financial Time Series

773

20.1 Introduction

773

20.2 SDEs: Background

774

20.3 Approximating Solutions to SDEs

775

20.4 S+ FinMetrics Functions for Solving SDEs

779

20.5 References

796

Generalized Method of Moments

798

21.1 Introduction

798

21.2 Single Equation Linear GMM

799

21.3 Estimation of S

806

21.4 GMM Estimation Using the S+ FinMetrics Function GMM

810

21.5 Hypothesis Testing for Linear Models

821

21.6 Nonlinear GMM

829

21.7 Examples of Nonlinear Models

832

21.8 References

855

Seminonparametric Conditional Density Models

859

22.1 Introduction

859

22.2 Overview of SNP Methodology

860

22.3 Estimating SNP Models in S+ FinMetrics

863

22.4 SNP Model Selection

892

22.5 SNP Model Diagnostics

903

22.6 Prediction from an SNP Model

909

22.7 Data Transformations

911

22.8 Examples

916

22.9 References

932

Efficient Method of Moments

935

23.1 Introduction

935

23.2 An Overview of the EMM Methodology

937

23.3 EMM Estimation in S+ FinMetrics

950

23.4 Examples

955

23.5 References

998

Index

1003