TIME SERIES ANALYSIS with MATLAB. CONDITIONAL VARIANCE MODELS: GARCH, EGARCH and GJR

MATLAB Econometrics Toolbox provides functions for modeling economic data You can select and calibrate economic models for simulation and forecasting Time series capabilities include univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis The toolbox provides Monte Carlo methods for simulating systems of linear and nonlinear stochastic differential equations and a variety of diagnostics for model selection, including hypothesis, unit root, and stationarity tests. This book develops, among others, the following topics Conditional Variance Models GARCH Model Specify GARCH Models Using garch GARCH Model Specifications GARCH Model with a Mean Offset GARCH Model with Nonconsecutive Lags GARCH Model with Known Parameter Values GARCH Model with a t Innovation Distributio EGARCH Model Specify EGARCH Models Using egarch EGARCH Model Specifications EGARCH Model with a Mean Offset EGARCH Model with Nonconsecutive Lags EGARCH Model with Known Parameter Values EGARCH Model with a t Innovation Distribution GJR Model Specify GJR Models Using gjr GJR Model Specifications GJR Model with a Mean Offset GJR Model with Nonconsecutive Lags GJR Model with Known Parameter Values GJR Model with a t Innovation Distribution Modify Properties of Conditional Variance Model Objects Specify the Conditional Variance Model Innovation Distribution Specify a Conditional Variance Model Maximum Likelihood Estimation for Conditional Variance Models Innovation Distribution Loglikelihood Functions Conditional Variance Model Estimation with Equality Constraints Presample Data for Conditional Variance Model Estimation Initial Values for Conditional Variance Model Estimation Optimization Settings for Conditional Variance Model Estimation Conditional Variance Model Constraints Infer Conditional Variances and Residuals Likelihood Ratio Test for Conditional Variance Models Compare Conditional Variance Models Using Information Criteria Monte Carlo Simulation of Conditional Variance Models Presample Data for Conditional Variance Model Simulation Simulate GARCH Models Assess the EGARCH Forecast Bias Using Simulations Simulate Conditional Variance Model Monte Carlo Forecasting of Conditional Variance Models MMSE Forecasting of Conditional Variance Models EGARCH MMSE Forecasts Forecast GJR Models Forecast Conditional Variance Model Including an Exogenous Regression Component ARMAX Model Specifying ARMAX Models Using garchset Maximum Likelihood Estimation Initial Parameter Values for Optimization GARCHFIT Examples Estimation Presample Data GARCHSIM Examples Simulation Presample Data MMSE Forecasting GARCHPRED Examples