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 conditional variance time series model|
|Conditional Variance Model Properties||Specify conditional variance model functional form and parameter values|
Create various GJR models.
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.
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.
Infer conditional variances from a fitted conditional variance model.
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.
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.