Robust M-estimation of multivariate GARCH models

Authors:
Kris Boudt;Christophe Croux
Affiliations:
Faculty of Business and Economics, K.U.Leuven, Belgium and Lessius University College, Belgium;Faculty of Business and Economics, K.U.Leuven, Belgium
Venue:
Computational Statistics & Data Analysis
Year:
2010

Citing 6
Cited 1

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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.