GARCH Model

Generalized, autoregressive, conditional heteroscedasticity models for volatility clustering

If positive and negative shocks of equal magnitude contribute equally to volatility, then you can model the innovations process using a GARCH model. For details on how to model volatility clustering using a GARCH model, seegarch.

Apps

Econometric Modeler

Analyze and model econometric time series

Functions

expand all

Create Model

garch GARCH conditional variance time series model

Fit Model to Data

`estimate`	Fit conditional variance model to data
`infer`	Infer conditional variances of conditional variance models
`summarize`	Display estimation results of conditional variance model

Generate Monte Carlo Simulations

`simulate`	Monte Carlo simulation of conditional variance models
`filter`	Filter disturbances through conditional variance model

Generate Minimum Mean Square Error Forecasts

forecast Forecast conditional variances from conditional variance models

Examples and How To

Create Model

Specify GARCH Models
Create GARCH models usinggarchor the Econometric Modeler app.
Modify Properties of Conditional Variance Models
使用点notat更改可修改的模型属性ion.
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

Select ARCH Lags for GARCH Model Using Econometric Modeler App
Interactively select the appropriate number of ARCH and GARCH lags for a GARCH model of daily Deutschmark/British pound foreign exchange rates.
Compare Conditional Variance Model Fit Statistics Using Econometric Modeler App
交互式地指定和适合GARCH、EGARCH和GJR models to data. Then, determine the model that fits to the data the best by comparing fit statistics.
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.
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.
Compare Conditional Variance Models Using Information Criteria
Compare the fits of several conditional variance models using AIC and BIC.
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.

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 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 or multivariate time series data.
Specifying Univariate Lag Operator Polynomials Interactively
Specify univariate 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.