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:	2005-12
Authors:	BAUWENS, Luc ; ROMBOUTS, Jeroen V.K.
Institutions:	Center for Operations Research and Econometrics (CORE), École des Sciences Économiques de Louvain
Subject:	ﬁnite mixture \| ML estimation \| Bayesian inference \| Value at Risk

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Extent:	application/pdf
Series:	CORE Discussion Papers.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series UNIVERSITE CATHOLIQUE DE LOUVAIN, Center for Operations Research and Econometrics (CORE) Number 2005085
Classification:	C11 - Bayesian Analysis ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C32 - Time-Series Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005008373