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Bayesian inference for the mixed conditional heteroskedasticity model

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Bauwens, Luc [UCL] Rombouts, Jeroen [UCL]

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.

Document type Article de périodique (Journal article)
Publication date 2007
Language Anglais
Journal information "Econometrics Journal" - Vol. 10, no. 2, p. 408-425 (Juillet 2007)
Peer reviewed yes
Publisher Wiley-Blackwell Publishing Ltd. (Oxford, United Kingdom)
issn 1368-4221
Publication status Publié
Affiliations UCL - EUEN/CORE - Center for operations research and econometrics
UCL - EUEN/CORE - Center for operations research and econometrics
Keywords Bayesian inference ; Finite mixture ; ML estimation ; Value at risk
Links
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Bibliographic reference Bauwens, Luc ; Rombouts, Jeroen. Bayesian inference for the mixed conditional heteroskedasticity model. In: Econometrics Journal, Vol. 10, no. 2, p. 408-425 (Juillet 2007)
Permanent URL http://hdl.handle.net/2078.1/23140