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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.doi:10.2139/ssrn.884424 fatcat:5irkrygsfzaflesm4wqul6duuy