Robust M-estimation of multivariate GARCH models

Kris Boudt*, Christophe Croux

*Corresponding author for this work

    Research output: Contribution to JournalArticleAcademicpeer-review

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)2459-2469
    Number of pages11
    JournalComputational Statistics and Data Analysis
    Volume54
    Issue number11
    DOIs
    Publication statusPublished - 1 Nov 2010

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