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Nonlinear models for autoregressive conditional heteroskedasticity


  • Timo Teräsvirta

    () (Aarhus University, School of Economics and Management and CREATES)


This paper contains a brief survey of nonlinear models of autoregressive conditional heteroskedasticity. The models in question are parametric nonlinear extensions of the original model by Engle (1982). After presenting the individual models, linearity testing and parameter estimation are discussed. Forecasting volatility with nonlinear models is considered. Finally, parametric nonlinear models based on multiplicative decomposition of the variance receive attention.

Suggested Citation

  • Timo Teräsvirta, 2011. "Nonlinear models for autoregressive conditional heteroskedasticity," CREATES Research Papers 2011-02, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2011-02

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    Cited by:

    1. Gallo, Giampiero M. & Otranto, Edoardo, 2015. "Forecasting realized volatility with changing average levels," International Journal of Forecasting, Elsevier, vol. 31(3), pages 620-634.

    More about this item


    nonlinear ARCH; nonlinear GARCH; neural network; nonlinear volatility; smooth transition GARCH; threshold GARCH.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection


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