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GJR Model

Glosten-Jagannathan-Runkle GARCH model for volatility clustering

If negative shocks contribute more to volatility than positive shocks, then you can model the innovations process using a GJR model and include leverage effects. For details on how to model volatility clustering using a GJR model, see gjr.

Apps

Econometric ModelerAnalyze and model econometric time series

Functions

expand all

gjrGJR conditional variance time series model
estimateFit conditional variance model to data
inferInfer conditional variances of conditional variance models
summarizeDisplay estimation results of conditional variance model
simulateMonte Carlo simulation of conditional variance models
filterFilter disturbances through conditional variance model
forecastForecast conditional variances from conditional variance models

Examples and How To

Create Model

Specify GJR Models

Create GJR models using gjr or the Econometric Modeler app.

Modify Properties of Conditional Variance Models

Change modifiable model properties using dot notation.

Specify the Conditional Variance Model Innovation Distribution

Specify Gaussian or t distributed innovations process.

Specify Conditional Variance Model For Exchange Rates

Create a conditional variance model for daily Deutschmark/British pound foreign exchange rates.

Specify Conditional Mean and Variance Models

Create a composite conditional mean and variance model.

Fit Model to Data

Compare Conditional Variance Model Fit Statistics Using Econometric Modeler App

Interactively specify and fit GARCH, EGARCH, and GJR models to data. Then, determine the model that fits to the data the best by comparing fit statistics.

Likelihood Ratio Test for Conditional Variance Models

Fit two competing, conditional variance models to data, and then compare their fits using a likelihood ratio test.

Estimate Conditional Mean and Variance Models

Estimate a composite conditional mean and variance model.

Perform GARCH Model Residual Diagnostics Using Econometric Modeler App

Interactively evaluate model assumptions after fitting data to a GARCH model by performing residual diagnostics.

Infer Conditional Variances and Residuals

Infer conditional variances from a fitted conditional variance model.

Share Results of Econometric Modeler App Session

Export variables to the MATLAB® Workspace, generate plain text and live functions that return a model estimated in an app session, or generate a report recording your activities on time series and estimated models in an Econometric Modeler app session.

Using Extreme Value Theory and Copulas to Evaluate Market Risk

This example shows how to model the market risk of a hypothetical global equity index portfolio with a Monte Carlo simulation technique using a Student's t copula and Extreme Value Theory (EVT).

Generate Monte Carlo Simulations

Simulate Conditional Variance Model

simulate a conditional variance model.

Simulate GARCH Models

Simulate from a GARCH process with and without specifying presample data.

Simulate Conditional Mean and Variance Models

Simulate responses and conditional variances from a composite conditional mean and variance model.

Generate Minimum Mean Square Error Forecasts

Forecast GJR Models

Generate MMSE forecasts from a GJR model.

Forecast a Conditional Variance Model

Forecast the Deutschmark/British pound foreign exchange rate using a fitted conditional variance model.

Forecast Conditional Mean and Variance Model

Forecast responses and conditional variances from a composite conditional mean and variance model.

Concepts

Econometric Modeler App Overview

The Econometric Modeler app is an interactive tool for visualizing and analyzing univariate time series data.

Specifying Lag Operator Polynomials Interactively

Specify lag operator polynomial terms for time series model estimation using Econometric Modeler.

Conditional Variance Models

Learn about models that account for volatility clustering.

Maximum Likelihood Estimation for Conditional Variance Models

Learn how maximum likelihood is carried out for conditional variance models.

Conditional Variance Model Estimation with Equality Constraints

Constrain the model during estimation using known parameter values.

Presample Data for Conditional Variance Model Estimation

Specify presample data to initialize the model.

Initial Values for Conditional Variance Model Estimation

Specify initial parameter values for estimation.

Optimization Settings for Conditional Variance Model Estimation

Troubleshoot estimation issues by specifying alternative optimization options.

Monte Carlo Simulation of Conditional Variance Models

Learn about Monte Carlo simulation.

Presample Data for Conditional Variance Model Simulation

Learn about presample requirements for simulation.

Monte Carlo Forecasting of Conditional Variance Models

Learn about Monte Carlo forecasting.

MMSE Forecasting of Conditional Variance Models

Learn about MMSE forecasting.