Bayesian inference for the mixed conditional heteroskedasticity model

Bauwens, Luc;Rombouts, Jeroen
(2005)

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  • Bauwens, Lucorcid-logoUCLouvain
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  • Rombouts, Jeroen
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Abstract
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.
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Bauwens, L., & Rombouts, J. (2005). Bayesian inference for the mixed conditional heteroskedasticity model (ECON Discussion Papers 2005/58). https://hdl.handle.net/2078.5/128661