Bayesian inference for the mixed conditional heteroskedasticity model

Luc Bauwens and Jeroen Rombouts

Econometrics Journal, 2007, vol. 10, issue 2, 408-425

Abstract: We estimate by Bayesian inference the mixed conditional heteroskedasticity model of Haas et al. (2004a Journal of Financial Econometrics 2, 211--50). 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. Copyright Royal Economic Society 2007

Date: 2007
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Related works:
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)
Working Paper: Bayesian inference for the mixed conditional heteroskedasticity model (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:ect:emjrnl:v:10:y:2007:i:2:p:408-425

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