Modeling financial time series with S-plus /

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Bibliographic Details
Author / Creator:Zivot, Eric.
Edition:2nd ed.
Imprint:New York, NY : Springer, c2006.
Description:1 online resource (xxii, 998 p.) : ill. cm.
Language:English
Series:International Federation for Information Processing (Series) ; 191.
Subject:S-Plus.
S-PLUS.
S-Plus.
Finance -- Mathematical models.
Time-series analysis.
Finance -- Econometric models.
Finances -- Modèles mathématiques.
Série chronologique.
Finances -- Modèles économétriques.
BUSINESS & ECONOMICS -- Finance.
Finance -- Econometric models.
Finance -- Mathematical models.
Time-series analysis.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/8877501
Hidden Bibliographic Details
Other authors / contributors:Wang, Jiahui.
ISBN:9780387279657
0387279652
0387323481 (electronic bk.)
9780387323480 (electronic bk.)
9786610189823
661018982X
0387955496 (Paper)
9780387955490 (Paper)
0387217630 (e-ISBN)
9780387217635 (e-ISBN)
Notes:Includes bibliographical references and index.
Summary:"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. This 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."--Jacket.
Other form:Print version: Zivot, Eric. Modeling financial time series with S-plus. 2nd ed. New York, NY : Springer, c2006 0387279652 0387217630
Table of Contents:
  • Preface
  • 1.3.2. Method
  • 6.5.3. The Newey-West Heteroskedasticity and Autocorrelation Consistent (HAC) Covariance Matrix Estimate
  • 6.6. Recursive Least Squares Estimation
  • 6.6.1. CUSUM and CUSUMSQ Tests for Parameter Stability
  • 6.6.2. Computing Recursive Least Squares Estimates Using the S+FinMetrics Function RLS
  • 6.7. References
  • 7. Univariate GARCH Modeling
  • 7.1. Introduction
  • 7.2. The Basic ARCH Model
  • 7.2.1. Testing for ARCH Effects
  • 7.3. The GARCH Model and Its Properties
  • 1.4. S-PLUS Resources
  • 7.3.1. ARMA Representation of GARCH Model
  • 7.3.2. GARCH Model and Stylized Facts
  • 7.4. GARCH Modeling Using S+FinMetrics
  • 7.4.1. GARCH Model Estimation
  • 7.4.2. GARCH Model Diagnostics
  • 7.5. GARCH Model Extensions
  • 7.5.1. Asymmetric Leverage Effects and News Impact
  • 7.5.2. Two Components Model
  • 7.5.3. GARCH-in-the-Mean Model
  • 7.5.4. ARMA Terms and Exogenous Variables in Conditional Mean Equation
  • 1.4.1. Books
  • 7.5.5. Exogenous Explanatory Variables in the Conditional Variance Equation
  • 7.5.6. Non-Gaussian Error Distributions
  • 7.6. GARCH Model Selection and Comparison
  • 7.6.1. Constrained GARCH Estimation
  • 7.7. GARCH Model Prediction
  • 7.8. GARCH Model Simulation
  • 7.9. Conclusion
  • 7.10. References
  • 8. Long Memory Time Series Modeling
  • 8.1. Introduction
  • 1.4.2. Internet
  • 8.2. Long Memory Time Series
  • 8.3. Statistical Tests for Long Memory
  • 8.3.1. R/S Statistic
  • 8.3.2. GPH Test
  • 8.4. Estimation of Long Memory Parameter
  • 8.4.1. R/S Analysis
  • 8.4.2. Periodogram Method
  • 8.4.3. Whittle's Method
  • 8.5. Estimation of FARIMA and SEMIFAR Models
  • 8.5.1. Fractional ARIMA Models
  • 1.5. References
  • 8.5.2. SEMIFAR Model
  • 8.6. Long Memory GARCH Models
  • 8.6.1. FIGARCH and FIEGARCH Models
  • 8.6.2. Estimation of Long Memory GARCH Models
  • 8.6.3. Custom Estimation of Long Memory GARCH Models
  • 8.7. Prediction from Long Memory Models
  • 8.7.1. Prediction from FARIMA/SEMIFAR Models
  • 8.7.2. Prediction from FIGARCH/FIEGARCH Models
  • 8.8. References
  • 9. Rolling Analysis of Time Series
  • 2. Time Series Specification, Manipulation, and Visualization in S-PLUS
  • 9.1. Introduction
  • 9.2. Rolling Descriptive Statistics
  • 9.2.1. Univariate Statistics
  • 9.2.2. Bivariate Statistics
  • 9.2.3. Exponentially Weighted Moving Averages
  • 9.2.4. Moving Average Methods for Irregularly Spaced High Frequency Data
  • 9.2.5. Rolling Analysis of Miscellaneous Functions
  • 9.3. Technical Analysis Indicators
  • 9.3.1. Price Indicators
  • 9.3.2. Momentum Indicators and Oscillators
  • 2.1. Introduction
  • 9.3.3. Volatility Indicators
  • 9.3.4. Volume Indicators
  • 9.4. Rolling Regression
  • 9.4.1. Estimating Rolling Regressions Using the S+FinMetrics Function rollOLS
  • 9.4.2. Rolling Predictions and Backtesting
  • 9.5. Rolling Analysis of General Models Using the S+FinMetrics Function roll
  • 9.6. References
  • 10. Systems of Regression Equations
  • 10.1. Introduction
  • 10.2. Systems of Regression Equations
  • 2.2. The Specification of "timeSeries" Objects in S-PLUS
  • 10.3. Linear Seemingly Unrelated Regressions
  • 10.3.1. Estimation
  • 10.3.2. Analysis of SUR Models with the S+FinMetrics Function SUR
  • 10.4. Nonlinear Seemingly Unrelated Regression Models
  • 10.4.1. Analysis of Nonlinear SUR Models with the S+FinMetrics Function NLSUR
  • 10.5. References
  • 11. Vector Autoregressive Models for Multivariate Time Series
  • 11.1. Introduction
  • 11.2. The Stationary Vector Autoregression Model
  • 11.2.1. Estimation
  • 2.2.1. Basic Manipulations
  • 11.2.2. Inference on Coefficients
  • 11.2.3. Lag Length Selection
  • 11.2.4. Estimating VAR Models Using the S+FinMetrics Function VAR
  • 11.3. Forecasting
  • 11.3.1. Traditional Forecasting Algorithm
  • 11.3.2. Simulation-Based Forecasting
  • 11.4. Structural Analysis
  • 11.4.1. Granger Causality
  • 11.4.2. Impulse Response Functions
  • 11.4.3. Forecast Error Variance Decompositions
  • 2.2.2. S-PLUS "timeDate" Objects
  • 11.5. An Extended Example
  • 11.6. Bayesian Vector Autoregression
  • 11.6.1. An Example of a Bayesian VAR Model
  • 11.6.2. Conditional Forecasts
  • 11.7. References
  • 12. Cointegration
  • 12.1. Introduction
  • 12.2. Spurious Regression and Cointegration
  • 12.2.1. Spurious Regression
  • 12.2.2. Cointegration
  • 1. S and S-PLUS
  • 2.2.3. Creating Common "timeDate" Sequences
  • 12.2.3. Cointegration and Common Trends
  • 12.2.4. Simulating Cointegrated Systems
  • 12.2.5. Cointegration and Error Correction Models
  • 12.3. Residual-Based Tests for Cointegration
  • 12.3.1. Testing for Cointegration When the Cointegrating Vector Is Pre-specified
  • 12.3.2. Testing for Cointegration When the Cointegrating Vector Is Estimated
  • 12.4. Regression-Based Estimates of Cointegrating Vectors and Error Correction Models
  • 12.4.1. Least Square Estimator
  • 12.4.2. Stock and Watson's Efficient Lead/Lag Estimator
  • 12.4.3. Estimating Error Correction Models by Least Squares
  • 2.2.4. Miscellaneous Time and Date Functions
  • 12.5. VAR Models and Cointegration
  • 12.5.1. The Cointegrated VAR
  • 12.5.2. Johansen's Methodology for Modeling Cointegration
  • 12.5.3. Specification of Deterministic Terms
  • 12.5.4. Likelihood Ratio Tests for the Number of Cointegrating Vectors
  • 12.5.5. Testing Hypothesis on Cointegrating Vectors Using the S+FinMetrics Function coint
  • 12.5.6. Maximum Likelihood Estimation of the Cointegrated VECM
  • 12.5.7. Maximum Likelihood Estimation of the Cointegrated VECM Using the S+FinMetrics Function VECM
  • 12.5.8. Forecasting from the VECM
  • 12.6. Appendix: Maximum Likelihood Estimation of a Cointegrated VECM
  • 2.2.5. Creating "timeSeries" Objects
  • 12.7. References
  • 13. 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
  • 13.4.1. Multivariate GARCH Model Estimation
  • 13.4.2. Multivariate GARCH Model Diagnostics
  • 13.5. Multivariate GARCH Model Extensions
  • 13.5.1. Matrix-Diagonal Models
  • 2.2.6. Aggregating and Disaggregating Time Series
  • 13.5.2. BEKK Models
  • 13.5.3. Univariate GARCH-based Models
  • 13.5.4. ARMA Terms and Exogenous Variables
  • 13.5.5. Multivariate Conditional t-Distribution
  • 13.6. Multivariate GARCH Prediction
  • 13.7. Custom Estimation of GARCH Models
  • 13.7.1. GARCH Model Objects
  • 13.7.2. Revision of GARCH Model Estimation
  • 13.8. Multivariate GARCH Model Simulation
  • 13.9. References
  • 2.2.7. Merging Time Series
  • 14. State Space Models
  • 14.1. Introduction
  • 14.2. State Space Representation
  • 14.2.1. Initial Conditions
  • 14.2.2. State Space Representation in S+FinMetrics/SsfPack
  • 14.2.3. Missing Values
  • 14.2.4. S+FinMetrics/SsfPack Functions for Specifying the State Space Form for Some Common Time Series Models
  • 14.2.5. Simulating Observations from the State Space Model
  • 14.3. Algorithms
  • 14.3.1. Kalman Filter
  • 2.2.8. Dealing with Missing Values Using the S+FinMetrics Function interpNA
  • 14.3.2. Kalman Smoother
  • 14.3.3. Smoothed State and Response Estimates
  • 14.3.4. Smoothed Disturbance Estimates
  • 14.3.5. Forecasting
  • 14.3.6. S+FinMetrics/SsfPack Implementation of State Space Modeling Algorithms
  • 14.4. Estimation of State Space Models
  • 14.4.1. Prediction Error Decomposition of Log-Likelihood
  • 14.4.2. Fitting State Space Models Using the S+FinMetrics/SsfPack Function SsfFit
  • 14.4.3. Quasi-Maximum Likelihood Estimation
  • 14.5. Simulation Smoothing
  • 2.3. Time Series Manipulation in S-PLUS
  • 14.6. References
  • 15. Factor Models for Asset Returns
  • 15.1. Introduction
  • 15.2. Factor Model Specification
  • 15.3. Macroeconomic Factor Models for Returns
  • 15.3.1. Sharpe's Single Index Model
  • 15.3.2. The General Multifactor Model
  • 15.4. Fundamental Factor Model
  • 15.4.1. BARRA-type Single Factor Model
  • 15.4.2. BARRA-type Industry Factor Model
  • 2.3.1. Creating Lags and Differences
  • 15.5. Statistical Factor Models for Returns
  • 15.5.1. Factor Analysis
  • 15.5.2. Principal Components
  • 15.5.3. Asymptotic Principal Components
  • 15.5.4. Determining the Number of Factors
  • 15.6. References
  • 16. Term Structure of Interest Rates
  • 16.1. Introduction
  • 16.2. Discount, Spot and Forward Rates
  • 16.2.1. Definitions and Rate Conversion
  • 2.3.2. Return Definitions
  • 16.2.2. Rate Conversion in S+FinMetrics
  • 16.3. Quadratic and Cubic Spline Interpolation
  • 16.4. Smoothing Spline Interpolation
  • 16.5. Nelson-Siegel Function
  • 16.6. Conclusion
  • 16.7. References
  • 17. Robust Change Detection
  • 17.1. Introduction
  • 17.2. REGARIMA Models
  • 17.3. Robust Fitting of REGARIMA Models
  • 2.3.3. Computing Asset Returns Using the S+FinMetrics Function getReturns
  • 17.4. Prediction Using REGARIMA Models
  • 17.5. Controlling Robust Fitting of REGARIMA Models
  • 17.5.1. Adding Seasonal Effects
  • 17.5.2. Controlling Outlier Detection
  • 17.5.3. Iterating the Procedure
  • 17.6. Algorithms of Filtered [tau]-Estimation
  • 17.6.1. Classical Maximum Likelihood Estimates
  • 17.6.2. Filtered [tau]-Estimates
  • 17.7. References
  • 18. Nonlinear Time Series Models
  • 1.1. Introduction
  • 2.4. Visualizing Time Series in S-PLUS
  • 18.1. Introduction
  • 18.2. BDS Test for Nonlinearity
  • 18.2.1. BDS Test Statistic
  • 18.2.2. Size of BDS Test
  • 18.2.3. BDS Test as a Nonlinearity Test and a Misspecification Test
  • 18.3. Threshold Autoregressive Models
  • 18.3.1. TAR and SETAR Models
  • 18.3.2. Tsay's Approach
  • 18.3.3. Hansen's Approach
  • 18.4. Smooth Transition Autoregressive Models
  • 2.4.1. Plotting "timeSeries" Using the S-PLUS Generic plot Function
  • 18.4.1. Logistic and Exponential STAR Models
  • 18.4.2. Test for STAR Nonlinearity
  • 18.4.3. Estimation of STAR Models
  • 18.5. Markov Switching State Space Models
  • 18.5.1. Discrete State Markov Process
  • 18.5.2. Markov Switching AR Process
  • 18.5.3. Markov Switching State Space Models
  • 18.6. An Extended Example: Markov Switching Coincident Index
  • 18.6.1. State Space Representation of Markov Switching Coincident Index Model
  • 18.6.2. Approximate MLE of Markov Switching Coincident Index
  • 2.4.2. Plotting "timeSeries" Using the S+FinMetrics Trellis Plotting Functions
  • 18.7. References
  • 19. Copulas
  • 19.1. Introduction
  • 19.2. Motivating Example
  • 19.3. Definitions and Basic Properties of Copulas
  • 19.3.1. Properties of Distributions
  • 19.3.2. Copulas and Sklar's Theorem
  • 19.3.3. Dependence Measures and Copulas
  • 19.4. Parametric Copula Classes and Families
  • 19.4.1. Normal Copula
  • 2.5. References
  • 19.4.2. Normal Mixture Copula
  • 19.4.3. Extreme Value Copula Class
  • 19.4.4. Archimedean Copulas
  • 19.4.5. Archimax Copulas
  • 19.4.6. Representation of Copulas in S+FinMetrics
  • 19.4.7. Creating Arbitrary Bivariate Distributions
  • 19.4.8. Simulating from Arbitrary Bivariate Distributions
  • 19.5. Fitting Copulas to Data
  • 19.5.1. Empirical Copula
  • 19.5.2. Maximum Likelihood Estimation
  • 3. Time Series Concepts
  • 19.5.3. Fitting Copulas Using the S+FinMetrics/EVANESCE Function fit.copula
  • 19.6. Risk Management Using Copulas
  • 19.6.1. Computing Portfolio Risk Measures Using Copulas
  • 19.6.2. Computing VaR and ES by Simulation
  • 19.7. References
  • 20. 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
  • 3.1. Introduction
  • 20.4.1. Problem-Specific Simulators
  • 20.4.2. General Simulators
  • 20.5. References
  • 21. Generalized Method of Moments
  • 21.1. Introduction
  • 21.2. Single Equation Linear GMM
  • 21.2.1. Definition of the GMM Estimator
  • 21.2.2. Specification Tests in Overidentified Models
  • 21.2.3. Two-Stage Least Squares as an Efficient GMM Estimator
  • 21.3. Estimation of S
  • 3.2. Univariate Time Series
  • 21.3.1. Serially Uncorrelated Moments
  • 21.3.2. Serially Correlated Moments
  • 21.3.3. Estimating S Using the S+FinMetrics Function var.hac
  • 21.4. GMM Estimation Using the S+FinMetrics Function GMM
  • 21.5. Hypothesis Testing for Linear Models
  • 21.5.1. Testing Restrictions on Coefficients
  • 21.5.2. Testing Subsets of Orthogonality Conditions
  • 21.5.3. Testing Instrument Relevance
  • 21.6. Nonlinear GMM
  • 21.6.1. Asymptotic Properties
  • 3.2.1. Stationary and Ergodic Time Series
  • 21.6.2. Hypothesis Tests for Nonlinear Models
  • 21.7. Examples of Nonlinear Models
  • 21.7.1. Student's t Distribution
  • 21.7.2. MA(1) Model
  • 21.7.3. Euler Equation Asset Pricing Model
  • 21.7.4. Stochastic Volatility Model
  • 21.7.5. Interest Rate Diffusion Model
  • 21.8. References
  • 22. Seminonparametric Conditional Density Models
  • 22.1. Introduction
  • 3.2.2. Linear Processes and ARMA Models
  • 22.2. Overview of SNP Methodology
  • 22.3. Estimating SNP Models in S+FinMetrics
  • 22.3.1. Example Data
  • 22.3.2. Markovian Time Series and the Gaussian Vector Autoregression Model
  • 22.3.3. Hermite Expansion and the Semiparametric VAR
  • 22.3.4. Conditional Heterogeneity
  • 22.3.5. ARCH/GARCH Leading Term
  • 22.4. SNP Model Selection
  • 22.4.1. Random Restarts
  • 22.4.2. The expand Function
  • 3.2.3. Autoregressive Models
  • 22.4.3. The SNP.auto Function
  • 22.5. SNP Model Diagnostics
  • 22.5.1. Residual Analysis
  • 22.5.2. Simulation
  • 22.6. Prediction from an SNP Model
  • 22.7. Data Transformations
  • 22.7.1. Centering and Scaling Transformation
  • 22.7.2. Transformations to Deal with Heavy Tailed Data
  • 22.7.3. Transformation to Deal with Small SNP Density Values
  • 22.8. Examples
  • 1.2. S Objects
  • 3.2.4. Moving Average Models
  • 22.8.1. SNP Models for Daily Returns on Microsoft Stock
  • 22.8.2. SNP Models for Daily Returns on the S&P 500 Index
  • 22.8.3. SNP Models for Weekly 3-Month U.S. T-Bill Rates
  • 22.9. References
  • 23. Efficient Method of Moments
  • 23.1. Introduction
  • 23.2. An Overview of the EMM Methodology
  • 23.2.1. Continuous-Time Stochastic Volatility Model for Interest Rates
  • 23.2.2. Minimum Chi-Squared Estimators
  • 23.2.3. Efficiency Considerations
  • 3.2.5. ARMA(p,q) Models
  • 23.2.4. A General Purpose Auxiliary Model
  • 23.2.5. The Projection Step
  • 23.2.6. The Estimation Step
  • 23.3. EMM Estimation in S+FinMetrics
  • 23.3.1. Simulator Functions
  • 23.3.2. SNP Auxiliary Model Estimation
  • 23.4. Examples
  • 23.4.1. MA(1) Model
  • 23.4.2. Discrete-Time Stochastic Volatility Models
  • 23.4.3. Interest Rate Diffusion Models
  • 3.2.6. Estimation of ARMA Models and Forecasting
  • 23.5. References
  • Index
  • 3.2.7. Martingales and Martingale Difference Sequences
  • 3.2.8. Long-run Variance
  • 3.2.9. Variance Ratios
  • 3.3. Univariate Nonstationary Time Series
  • 3.4. Long Memory Time Series
  • 3.5. Multivariate Time Series
  • 3.5.1. Stationary and Ergodic Multivariate Time Series
  • 1.2.1. Assignment
  • 3.5.2. Multivariate Wold Representation
  • 3.5.3. Long Run Variance
  • 3.6. References
  • 4. Unit Root Tests
  • 4.1. Introduction
  • 4.2. Testing for Nonstationarity and Stationarity
  • 4.3. Autoregressive Unit Root Tests
  • 4.3.1. Simulating the DF and Normalized Bias Distributions
  • 4.3.2. Trend Cases
  • 4.3.3. Dickey-Fuller Unit Root Tests
  • 1.2.2. Class
  • 4.3.4. Phillips-Perron Unit Root Tests
  • 4.4. Stationarity Tests
  • 4.4.1. Simulating the KPSS Distributions
  • 4.4.2. Testing for Stationarity Using the S+FinMetrics Function stationaryTest
  • 4.5. Some Problems with Unit Root Tests
  • 4.6. Efficient Unit Root Tests
  • 4.6.1. Point Optimal Tests
  • 4.6.2. DF-GLS Tests
  • 4.6.3. Modified Efficient PP Tests
  • 4.6.4. Estimating [lambda superscript 2]
  • 1.2.3. Method
  • 4.6.5. Choosing Lag Lengths to Achieve Good Size and Power
  • 4.7. References
  • 5. Modeling Extreme Values
  • 5.1. Introduction
  • 5.2. Modeling Maxima and Worst Cases
  • 5.2.1. The Fisher-Tippet Theorem and the Generalized Extreme Value Distribution
  • 5.2.2. Estimation of the GEV Distribution
  • 5.2.3. Return Level
  • 5.3. Modeling Extremes Over High Thresholds
  • 5.3.1. The Limiting Distribution of Extremes Over High Thresholds and the Generalized Pareto Distribution
  • 1.3. Modeling Functions in S+FinMetrics
  • 5.3.2. Estimating the GPD by Maximum Likelihood
  • 5.3.3. Estimating the Tails of the Loss Distribution
  • 5.3.4. Risk Measures
  • 5.4. Hill's Non-parametric Estimator of Tail Index
  • 5.4.1. Hill Tail and Quantile Estimation
  • 5.5. References
  • 6. Time Series Regression Modeling
  • 6.1. Introduction
  • 6.2. Time Series Regression Model
  • 6.2.1. Least Squares Estimation
  • 1.3.1. Formula Specification
  • 6.2.2. Goodness of Fit
  • 6.2.3. Hypothesis Testing
  • 6.2.4. Residual Diagnostics
  • 6.3. Time Series Regression Using the S+FinMetrics Function OLS
  • 6.4. Dynamic Regression
  • 6.4.1. Distributed Lags and Polynomial Distributed Lags
  • 6.4.2. Polynomial Distributed Lag Models
  • 6.5. Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
  • 6.5.1. The Eicker-White Heteroskedasticity Consistent (HC) Covariance Matrix Estimate
  • 6.5.2. Testing for Heteroskedasticity