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Fat Tails and Asymmetry in Financial Volatility Models

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  • Peter Verhoeven

    (School of Economics and Finance, Curtin University of Technology)

  • Michael McAleer

    (Department of Economics, University of Western Australia)

Abstract

Although the GARCH model has been quite successful in capturing important empirical aspects of financial data, particularly for the symmetric effects of volatility, it has had far less success in capturing the effects of extreme observations, outliers and skewness in returns. This paper examines the GARCH model under various non-normal error distributions in order to evaluate skewness and leptokurtosis. The empirical results show that GARCH models estimated using asymmetric leptokurtic distributions are superior to their counterparts estimated under normality, in terms of: (i) capturing skewness and leptokurtosis; (ii) the maximized log-likelihood values; and (iii) isolating the ARCH and GARCH parameter estimates from the adverse effects of outliers. Overall, the flexible asymmetric Student-t distribution performs best in terms of capturing the non-normal aspects of the data.

Suggested Citation

  • Peter Verhoeven & Michael McAleer, 2003. "Fat Tails and Asymmetry in Financial Volatility Models," CIRJE F-Series CIRJE-F-211, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2003cf211
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    8. Zhu, Ke & Li, Wai Keung, 2013. "A new Pearson-type QMLE for conditionally heteroskedastic models," MPRA Paper 52344, University Library of Munich, Germany.
    9. Hayette Gatfaoui, 2010. "Capital Asset Pricing Model," Post-Print hal-00589904, HAL.
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    21. Gatfaoui, Hayette, 2013. "Translating financial integration into correlation risk: A weekly reporting's viewpoint for the volatility behavior of stock markets," Economic Modelling, Elsevier, vol. 30(C), pages 776-791.

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