Bayesian inference for the mixed conditional heteroskedasticity model

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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Year of publication:	2006-06
Authors:	Bauwens, Luc ; Rombouts, Jeroen V.K.
Institutions:	Institut d'Économie Appliquée, HEC Montréal (École des Hautes Études Commerciales)
Subject:	Finite mixture \| ML estimation \| bayesian inference \| value at risk

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Extent:	application/pdf
Series:	Cahiers de recherche.
Type of publication:	Book / Working Paper
Language:	English
Notes:	The price is Free Number 06-07 26 pages
Classification:	C11 - Bayesian Analysis ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C32 - Time-Series Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005651477