If positive and negative shocks of equal magnitude asymmetrically
contribute to volatility, then you can model the innovations process
using an EGARCH model and include leverage effects. For details on how
to model volatility clustering using an EGARCH model, see
|Econometric Modeler||Analyze and model econometric time series|
Create EGARCH models using
egarch 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.
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.
Fit two competing, conditional variance models to data, and then compare their fits using a likelihood ratio test.
Compare the fits of several conditional variance models using AIC and BIC.
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.
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.
Compare simulation-based forecasts to MMSE forecasts to assess bias.
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.