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Robust M-estimation of multivariate GARCH models

  • Boudt, Kris
  • Croux, Christophe
Registered author(s):

    The Gaussian quasi-maximum likelihood estimator of Multivariate GARCH models is shown to be very sensitive to outliers in the data. A class of robust M-estimators for MGARCH models is developed. To increase the robustness of the estimators, the use of volatility models with the property of bounded innovation propagation is recommended. The Monte Carlo study and an empirical application to stock returns document the good robustness properties of the M-estimator with a fat-tailed Student t loss function.

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    File URL: http://www.sciencedirect.com/science/article/B6V8V-4XP37MN-4/2/791036d15ffce2afb78b6a09da440e3c
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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 54 (2010)
    Issue (Month): 11 (November)
    Pages: 2459-2469

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    Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2459-2469
    Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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