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
|Econometric Modeler||Analyze and model econometric time series|
Create GJR models using
gjr or the Econometric Modeler
Change modifiable model properties using dot notation.
Specify Gaussian or t distributed innovations process.
Create a conditional variance model for daily Deutschmark/British pound foreign exchange rates.
Create a composite conditional mean and variance model.
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.
Fit two competing, conditional variance models to data, and then compare their fits using a likelihood ratio test.
Estimate a composite conditional mean and variance model.
Interactively evaluate model assumptions after fitting data to a GARCH model by performing residual diagnostics.
Infer conditional variances from a fitted conditional variance model.
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.
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).
simulate a conditional variance model.
Simulate from a GARCH process with and without specifying presample data.
Simulate responses and conditional variances from a composite conditional mean and variance model.
Generate MMSE forecasts from a GJR model.
Forecast the Deutschmark/British pound foreign exchange rate using a fitted conditional variance model.
Forecast responses and conditional variances from a composite conditional mean and variance model.
The Econometric Modeler app is an interactive tool for visualizing and analyzing univariate time series data.
Specify lag operator polynomial terms for time series model estimation using Econometric Modeler.
Learn about models that account for volatility clustering.
Learn how maximum likelihood is carried out for conditional variance models.
Constrain the model during estimation using known parameter values.
Specify presample data to initialize the model.
Specify initial parameter values for estimation.
Troubleshoot estimation issues by specifying alternative optimization options.
Learn about Monte Carlo simulation.
Learn about presample requirements for simulation.
Learn about Monte Carlo forecasting.
Learn about MMSE forecasting.