Bayesian Inference for the Mixed Conditional Heteroskedasticity Model
CORE Discussion Paper No. 2005/85
24 Pages Posted: 24 Feb 2006
Date Written: December 1, 2005
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.
Keywords: Finite mixture, ML estimation, Bayesian inference, Value at Risk.
JEL Classification: C11, C15, C32
Suggested Citation: Suggested Citation