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A diagnostic m-test for distributional specification of parametric conditional heteroscedasticity models for financial data

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  • Lejeune, Bernard

Abstract

This paper proposes a convenient and generally applicable diagnostic m-test for checking the distributional specification of parametric conditional heteroscedasticity models for financial data such as the customary Student t GARCH model. The proposed test is based on the moments of the probability integral transform of the innovations of the assumed model. Monte-Carlo evidence indicates that our test performs well both in terms of size and power. An empirical example illustrates the practical usefulness of the test and some of its possible extensions are outlined.

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  • Lejeune, Bernard, 2009. "A diagnostic m-test for distributional specification of parametric conditional heteroscedasticity models for financial data," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 507-523, June.
    • Handle: RePEc:eee:empfin:v:16:y:2009:i:3:p:507-523
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    3. J. Hambuckers & C. Heuchenne, 2017. "A robust statistical approach to select adequate error distributions for financial returns," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 137-161, January.
    4. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    5. Chen, Yi-Ting, 2012. "A simple approach to standardized-residuals-based higher-moment tests," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 427-453.

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