In empirical work on multivariate financial time series, it is common to postulate a Multivariate GARCH model. Due to measurement error or unusual economic events, some observations can be in discordance with the model assumptions. It is desirable that these outliers have little influence on the estimation result. In a Monte Carlo study, we show that the Gaussian quasi-maximum likelihood estimator can be highly affected by outliers. As a more robust alternative, we propose to use M-estimators. By downweighting extreme returns in the loss function of the M-estimator and in the MGARCH equation, we obtain robust parameter estimates and conditional covariance matrix predictions. We prove consistency of a wide class of M-estimators for MGARCH models with elliptical innovations. Simulations and a real data example document the benefits of the robust approach.
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number
4271.
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