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 Modeler | Analyze and model econometric time series |
Functions
Topics
Create Model
- Specify GJR Models
Create GJR models usinggjror the Econometric Modeler app. - Modify Properties of Conditional Variance Models
Change modifiable model properties using dot notation. - Specifying Univariate Lag Operator Polynomials Interactively
Specify univariate lag operator polynomial terms for time series model estimation using Econometric Modeler. - Specify 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
- Analyze Time Series Data Using Econometric Modeler
Interactively visualize and analyze univariate or multivariate time series 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 Model
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). - 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.
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. - 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.
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. - Monte Carlo Forecasting of Conditional Variance Models
Learn about Monte Carlo forecasting. - MMSE Forecasting of Conditional Variance Models
Learn about MMSE forecasting.
