Bayesian inference for the mixed conditional heteroskedasticity model

Luc Bauwens and Jeroen Rombouts

No 2005085, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: We estimate by Bayesian inference the mixed conditional heteroskedasticity model of (Haas, Mittnik, and Paolella 2004a). We construct a Gibbs sampler algorithm to compute posterior and predictive densities. The number of mixture components is selected by the marginal likelihood criterion. We apply the model to the SP500 daily returns.

Keywords: finite mixture; ML estimation; Bayesian inference; Value at Risk (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 (search for similar items in EconPapers)
Date: 2005-12
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Bayesian inference for the mixed conditional heteroskedasticity model (2007)
Working Paper: Bayesian inference for the mixed conditional heteroskedasticity model (2007)
Working Paper: Bayesian inference for the mixed conditional heteroskedasticity model (2006)
Working Paper: Bayesian inference for the mixed conditional heteroskedasticity model (2005)
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