Bayesian inference for the mixed conditional heteroskedasticity model. (English) Zbl 1122.62015
Summary: We estimate by Bayesian inference the mixed conditional heteroskedasticity model of M. Haas et al. [Mixed normal conditional heteroskedasticity. J. Financial Econ. 2, 211–250 (2004)]. 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.
MSC:
| 62F15 | Bayesian inference |
| 62P05 | Applications of statistics to actuarial sciences and financial mathematics |
References:
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