Modeling financial time series with S-plus /
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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: | |
Format: | E-Resource Book |
URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/8877501 |
Table of Contents:
- Preface
- 1. S and S-PLUS
- 1.1. Introduction
- 1.2. S Objects
- 1.2.1. Assignment
- 1.2.2. Class
- 1.2.3. Method
- 1.3. Modeling Functions in S+FinMetrics
- 1.3.1. Formula Specification
- 1.3.2. Method
- 1.4. S-PLUS Resources
- 1.4.1. Books
- 1.4.2. Internet
- 1.5. References
- 2. Time Series Specification, Manipulation, and Visualization in S-PLUS
- 2.1. Introduction
- 2.2. The Specification of "timeSeries" Objects in S-PLUS
- 2.2.1. Basic Manipulations
- 2.2.2. S-PLUS "timeDate" Objects
- 2.2.3. Creating Common "timeDate" Sequences
- 2.2.4. Miscellaneous Time and Date Functions
- 2.2.5. Creating "timeSeries" Objects
- 2.2.6. Aggregating and Disaggregating Time Series
- 2.2.7. Merging Time Series
- 2.2.8. Dealing with Missing Values Using the S+FinMetrics Function interpNA
- 2.3. Time Series Manipulation in S-PLUS
- 2.3.1. Creating Lags and Differences
- 2.3.2. Return Definitions
- 2.3.3. Computing Asset Returns Using the S+FinMetrics Function getReturns
- 2.4. Visualizing Time Series in S-PLUS
- 2.4.1. Plotting "timeSeries" Using the S-PLUS Generic plot Function
- 2.4.2. Plotting "timeSeries" Using the S+FinMetrics Trellis Plotting Functions
- 2.5. References
- 3. Time Series Concepts
- 3.1. Introduction
- 3.2. Univariate Time Series
- 3.2.1. Stationary and Ergodic Time Series
- 3.2.2. Linear Processes and ARMA Models
- 3.2.3. Autoregressive Models
- 3.2.4. Moving Average Models
- 3.2.5. ARMA(p,q) Models
- 3.2.6. Estimation of ARMA Models and Forecasting
- 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
- 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
- 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]
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 23.5. References
- Index