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.

Multivariate Markov switching dynamic conditional correlation GARCH representations for contagion analysis

CAPORIN, MASSIMILIANO
2005

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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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/1421663
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