Abstract.
This paper provides an extension of the Dynamic Conditional Correlation model of Engle (2002) by allowing both the unconditional correlation and the parameters to be driven by an unobservable Markov chain. We provide the estimation algorithm and perform an empirical analysis of the contagion phenomenon in which our model is compared to the traditional CCC and DCC representations.
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We acknowledge financial support from the Italian national research project on "The Euro and European financial market volatility: contagion, interdependence and volatility transmission" financed by the Italian Ministry of University and Research. Furthermore, we thank William De Pieri for research assistance and are grateful to Loriana Pelizzon, Claudio Pizzi, Domenico Sartore and the participants at the Forecasting Financial Markets 2004 conference and at the XLII Annual Meeting of the Italian Statistical Society for helpful comments. Usual disclaimer applies.
Correspondence to: Monica Bilio
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Billio, M., Caporin, M. Multivariate Markov switching dynamic conditional correlation GARCH representations for contagion analysis. JISS 14, 145–161 (2005). https://doi.org/10.1007/s10260-005-0108-8
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DOI: https://doi.org/10.1007/s10260-005-0108-8