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Quasi-maximum likelihood estimation in generalized polynomial autoregressive conditional heteroscedasticity models

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  • Tinkl, Fabian

Abstract

In this article, consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) in the class of polynomial augmented generalized autoregressive conditional heteroscedasticity models (GARCH) is proven. The result extends the results of the standard GARCH model to the class of polynomial augmented GARCH models which contains many commonly employed GARCH models as special cases. The results are obtained under mild conditions.

Suggested Citation

  • Tinkl, Fabian, 2013. "Quasi-maximum likelihood estimation in generalized polynomial autoregressive conditional heteroscedasticity models," FAU Discussion Papers in Economics 03/2013, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, revised 2013.
    • Handle: RePEc:zbw:iwqwdp:032013
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    Cited by:

    1. Leucht, Anne & Neumann, Michael H. & Kreiss, Jens-Peter, 2013. "A model specification test for GARCH(1,1) processes," Working Papers 13-11, University of Mannheim, Department of Economics.
    2. Anne Leucht & Jens-Peter Kreiss & Michael H. Neumann, 2015. "A Model Specification Test For GARCH(1,1) Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1167-1193, December.

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    Keywords

    asymptotic normality; consistency; polynomial augmented GARCH models; quasi-maximum likelihood estimation;
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