Zivot / Wang Modeling Financial Time Series with S-PLUS®

2. Auflage 2006
ISBN: 978-0-387-32348-0
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 998 Seiten

This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. It is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. This edition covers S+FinMetrics 2.0 and includes new chapters.

Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department, and adjunct associate professor of finance in the Business School at the University of Washington. He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T. Buechel Award for Outstanding Teaching. He is an associate editor of Studies in Nonlinear Dynamics and Econometrics. He has published papers in the leading econometrics journals, including Econometrica, Econometric Theory, the Journal of Business and Economic Statistics, Journal of Econometrics, and the Review of Economics and Statistics. Jiahui Wang is an employee of Ronin Capital LLC. He received a Ph.D. in Economics from the University of Washington in 1997. He has published in leading econometrics journals such as Econometrica and Journal of Business and Economic Statistics, and is the Principal Investigator of National Science Foundation SBIR grants. In 2002 Dr. Wang was selected as one of the '2000 Outstanding Scholars of the 21st Century' by International Biographical Centre.

Autoren/Hrsg.

Zivot, Eric

Wang, Jiahui

Inhaltsverzeichnis

1;Preface;5
1.1;References;10
2;Contents;11
3;S and S- PLUS;23
3.1;1.1 Introduction;23
3.2;1.2 S Objects;24
3.3;1.3 Modeling Functions in S+ FinMetrics;30
3.4;1.4 S- PLUS Resources;34
3.5;1.5 References;35
4;Time Series Specification, Manipulation, and Visualization in S- PLUS;37
4.1;2.1 Introduction;37
4.2;2.2 The Specification of "timeSeries” Objects in S- PLUS;37
4.3;2.3 Time Series Manipulation in S- PLUS;62
4.4;2.4 Visualizing Time Series in S- PLUS;70
4.5;2.5 References;77
5;Time Series Concepts;78
5.1;3.1 Introduction;78
5.2;3.2 Univariate Time Series;79
5.3;3.3 Univariate Nonstationary Time Series;114
5.4;3.4 Long Memory Time Series;118
5.5;3.5 Multivariate Time Series;122
5.6;3.6 References;130
6;Unit Root Tests;132
6.1;4.1 Introduction;132
6.2;4.2 Testing for Nonstationarity and Stationarity;133
6.3;4.3 Autoregressive Unit Root Tests;135
6.4;4.4 Stationarity Tests;150
6.5;4.5 Some Problems with Unit Root Tests;153
6.6;4.6 Efficient Unit Root Tests;153
6.7;4.7 References;159
7;Modeling Extreme Values;161
7.1;5.1 Introduction;161
7.2;5.2 Modeling Maxima and Worst Cases;162
7.3;5.3 Modeling Extremes Over High Thresholds;177
7.4;5.4 Hill’s Non-parametric Estimator of Tail Index;194
7.5;5.5 References;198
8;Time Series Regression Modeling;200
8.1;6.1 Introduction;200
8.2;6.2 Time Series Regression Model;201
8.3;6.3 Time Series Regression Using the S+ FinMetrics Function OLS;204
8.4;6.4 Dynamic Regression;220
8.5;6.5 Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation;227
8.6;6.6 Recursive Least Squares Estimation;236
8.7;6.7 References;240
9;Univariate GARCH Modeling;242
9.1;7.1 Introduction;242
9.2;7.2 The Basic ARCH Model;243
9.3;7.3 The GARCH Model and Its Properties;248
9.4;7.4 GARCH Modeling Using S+ FinMetrics;251
9.5;7.5 GARCH Model Extensions;259
9.6;7.6 GARCH Model Selection and Comparison;279
9.7;7.7 GARCH Model Prediction;281
9.8;7.8 GARCH Model Simulation;284
9.9;7.9 Conclusion;286
9.10;7.10 References;286
10;Long Memory Time Series Modeling;289
10.1;8.1 Introduction;289
10.2;8.2 Long Memory Time Series;290
10.3;8.3 Statistical Tests for Long Memory;294
10.4;8.4 Estimation of Long Memory Parameter;298
10.5;8.5 Estimation of FARIMA and SEMIFAR Models;302
10.6;8.6 Long Memory GARCH Models;314
10.7;8.7 Prediction from Long Memory Models;322
10.8;8.8 References;327
11;Rolling Analysis of Time Series;330
11.1;9.1 Introduction;330
11.2;9.2 Rolling Descriptive Statistics;331
11.3;9.3 Technical Analysis Indicators;354
11.4;9.4 Rolling Regression;359
11.5;9.5 Rolling Analysis of General Models Using the S+ FinMetrics Function roll;375
11.6;9.6 References;377
12;Systems of Regression Equations;378
12.1;10.1 Introduction;378
12.2;10.2 Systems of Regression Equations;379
12.3;10.3 Linear Seemingly Unrelated Regressions;381
12.4;10.4 Nonlinear Seemingly Unrelated Regression Models;391
12.5;10.5 References;399
13;Vector Autoregressive Models for Multivariate Time Series;401
13.1;11.1 Introduction;401
13.2;11.2 The Stationary Vector Autoregression Model;402
13.3;11.3 Forecasting;414
13.4;11.4 Structural Analysis;422
13.5;11.5 An Extended Example;432
13.6;11.6 Bayesian Vector Autoregression;440
13.7;11.7 References;444
14;Cointegration;446
14.1;12.1 Introduction;446
14.2;12.2 Spurious Regression and Cointegration;447
14.3;12.3 Residual-Based Tests for Cointegration;459
14.4;12.4 Regression-Based Estimates of Cointegrating Vectors and Error Correction Models;465
14.5;12.5 VAR Models and Cointegration;470
14.6;12.6 Appendix: Maximum Likelihood Estimation of a Cointegrated VECM;491
14.7;12.7 References;493
15;Multivariate GARCH Modeling;496
15.1;13.1 Introduction;496
15.2;13.2 Exponentially Weighted Covariance Estimate;497
15.3;13.3 Diagonal VEC Model;501
15.4;13.4 Multivariate GARCH Modeling in S+ FinMetrics;502
15.5;13.5 Multivariate GARCH Model Extensions;511
15.6;13.6 Multivariate GARCH Prediction;524
15.7;13.7 Custom Estimation of GARCH Models;527
15.8;13.8 Multivariate GARCH Model Simulation;530
15.9;13.9 References;532
16;State Space Models;534
16.1;14.1 Introduction;534
16.2;14.2 State Space Representation;535
16.3;14.3 Algorithms;558
16.4;14.4 Estimation of State Space Models;567
16.5;14.5 Simulation Smoothing;580
16.6;14.6 References;581
17;Factor Models for Asset Returns;583
17.1;15.1 Introduction;583
17.2;15.2 Factor Model Specification;584
17.3;15.3 Macroeconomic Factor Models for Returns;585
17.4;15.4 Fundamental Factor Model;594
17.5;15.5 Statistical Factor Models for Returns;604
17.6;15.6 References;628
18;Term Structure of Interest Rates;631
18.1;16.1 Introduction;631
18.2;16.2 Discount, Spot and Forward Rates;632
18.3;16.3 Quadratic and Cubic Spline Interpolation;634
18.4;16.4 Smoothing Spline Interpolation;638
18.5;16.5 Nelson-Siegel Function;642
18.6;16.6 Conclusion;646
18.7;16.7 References;647
19;Robust Change Detection;649
19.1;17.1 Introduction;649
19.2;17.2 REGARIMA Models;650
19.3;17.3 Robust Fitting of REGARIMA Models;651
19.4;17.4 Prediction Using REGARIMA Models;656
19.5;17.5 Controlling Robust Fitting of REGARIMA Models;657
19.6;17.6 Algorithms of Filtered Filtered -Estimation;663
19.7;17.7 References;665
20;Nonlinear Time Series Models;667
20.1;18.1 Introduction;667
20.2;18.2 BDS Test for Nonlinearity;668
20.3;18.3 Threshold Autoregressive Models;676
20.4;18.4 Smooth Transition Autoregressive Models;692
20.5;18.5 Markov Switching State Space Models;701
20.6;18.6 An Extended Example: Markov Switching Coincident Index;715
20.7;18.7 References;723
21;Copulas;727
21.1;19.1 Introduction;727
21.2;19.2 Motivating Example;728
21.3;19.3 Definitions and Basic Properties of Copulas;736
21.4;19.4 Parametric Copula Classes and Families;743
21.5;19.5 Fitting Copulas to Data;761
21.6;19.6 Risk Management Using Copulas;768
21.7;19.7 References;771
22;Continuous-Time Models for Financial Time Series;773
22.1;20.1 Introduction;773
22.2;20.2 SDEs: Background;774
22.3;20.3 Approximating Solutions to SDEs;775
22.4;20.4 S+ FinMetrics Functions for Solving SDEs;779
22.5;20.5 References;796
23;Generalized Method of Moments;798
23.1;21.1 Introduction;798
23.2;21.2 Single Equation Linear GMM;799
23.3;21.3 Estimation of S;806
23.4;21.4 GMM Estimation Using the S+ FinMetrics Function GMM;810
23.5;21.5 Hypothesis Testing for Linear Models;821
23.6;21.6 Nonlinear GMM;829
23.7;21.7 Examples of Nonlinear Models;832
23.8;21.8 References;855
24;Seminonparametric Conditional Density Models;859
24.1;22.1 Introduction;859
24.2;22.2 Overview of SNP Methodology;860
24.3;22.3 Estimating SNP Models in S+ FinMetrics;863
24.4;22.4 SNP Model Selection;892
24.5;22.5 SNP Model Diagnostics;903
24.6;22.6 Prediction from an SNP Model;909
24.7;22.7 Data Transformations;911
24.8;22.8 Examples;916
24.9;22.9 References;932
25;Efficient Method of Moments;935
25.1;23.1 Introduction;935
25.2;23.2 An Overview of the EMM Methodology;937
25.3;23.3 EMM Estimation in S+ FinMetrics;950
25.4;23.4 Examples;955
25.5;23.5 References;998
26;Index;1003