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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, see garch.

Using Objects

garch GARCH conditional variance time series model

Functions

garch Create GARCH conditional variance model object
estimate Fit conditional variance model to data
infer Infer conditional variances of conditional variance models
print Display parameter estimation results for conditional variance models
simulate Monte Carlo simulation of conditional variance models
filter Filter disturbances through conditional variance model
forecast Forecast conditional variances from conditional variance models

Examples and How To

Create Model

Specify GARCH Models Using garch

Create various GARCH models.

Modify Properties of Conditional Variance Models

Change modifiable model properties using dot notation.

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

Estimate Conditional Mean and Variance Models

Estimate a composite conditional mean and variance model.

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.

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

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.

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