Bayesian inference for the mixed conditional heteroskedasticity model

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.

Tasks

Reproductions