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Robust M-estimation of multivariate GARCH models

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  • Boudt, Kris
  • Croux, Christophe

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.

Suggested Citation

  • Boudt, Kris & Croux, Christophe, 2010. "Robust M-estimation of multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2459-2469, November.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2459-2469
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    Cited by:

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    4. Laurent, Sébastien & Lecourt, Christelle & Palm, Franz C., 2016. "Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 383-400.
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    6. Elie I Bouri, 2013. "Correlation and Volatility of the MENA Equity Markets in Turbulent Periods, and Portfolio Implications," Economics Bulletin, AccessEcon, vol. 33(2), pages 1575-1593.
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    8. Kris Boudt & Jon Danielsson & Siem Jan Koopman & Andre Lucas, 2012. "Regime switches in the volatility and correlation of financial institutions," Working Paper Research 227, National Bank of Belgium.
    9. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
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    14. Patrick F. Patrocinio & Valderio A. Reisen & Pascal Bondon & Edson Z. Monte & Ian M. Danilevicz, 2024. "M-Quantile Estimation for GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2175-2192, June.
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