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Conditional Variance Models

GARCH, exponential GARCH (EGARCH), and GJR models

Conditional variance models attempt to address volatility clustering in univariate time series models to improve parameter estimates and forecast accuracy. To model volatility, Econometrics Toolbox™ supports the standard generalized autoregressive conditional heteroscedastic (ARCH/GARCH) model, the exponential GARCH (EGARCH) model, and the Glosten, Jagannathan, and Runkle (GJR) model.

To convert from the previous conditional variance model analysis syntaxes, see Converting from GARCH Functions to Model Objects.

  • GARCH Model
    Generalized, autoregressive, conditional heteroscedasticity models for volatility clustering
  • EGARCH Model
    Exponential, generalized, autoregressive, conditional heteroscedasticity models for volatility clustering.
  • GJR Model
    Glosten-Jagannathan-Runkle GARCH model for volatility clustering

Featured Examples