Bayesian Inference for the Mixed Conditional Heteroskedasticity Model

Luc Bauwens, J. V. K. Rombouts
2005 Social Science Research Network  
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