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Nonlinear models for autoregressive conditional heteroskedasticity


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  • 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.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-02.

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Length: 29
Date of creation: 05 Jan 2011
Date of revision:
Handle: RePEc:aah:create:2011-02

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

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