Bayesian inference for the mixed conditional heteroskedasticity model

Luc Bauwens and Jeroen Rombouts

No 2005058, Discussion Papers (ECON - Département des Sciences Economiques) from Université catholique de Louvain, Département des Sciences Economiques

Abstract: We estimate by Bayesian inference the mixed conditional heteroskedasticity model of (Haas, Mittnik and Paolelella 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 mixure; ML estimation; Bayesian inference; Value at Risk (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 (search for similar items in EconPapers)
Pages: 26
Date: 2005-12-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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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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