Bayesian multivariate GARCH models with dynamic correlations and asymmetric error distributions

Fioruci, Jose A.; Ehlers, Ricardo S.; Andrade Filho, Marinho G.

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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Bayesian multivariate GARCH models with dynamic correlations and asymmetric error distributions

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Author(s):	Fioruci, Jose A. ^[1] ; Ehlers, Ricardo S. ^[1] ; Andrade Filho, Marinho G. ^[1] Total Authors: 3
Affiliation:	^[1] Univ Sao Paulo, Dept Appl Math & Stat, Sao Carlos, SP - Brazil Total Affiliations: 1
Document type:	Journal article
Source:	Journal of Applied Statistics; v. 41, n. 2, p. 320-331, FEB 1 2014.
Web of Science Citations:	3
Abstract
The main goal in this paper is to develop and apply stochastic simulation techniques for GARCH models with multivariate skewed distributions using the Bayesian approach. Both parameter estimation and model comparison are not trivial tasks and several approximate and computationally intensive methods (Markov chain Monte Carlo) will be used to this end. We consider a flexible class of multivariate distributions which can model both skewness and heavy tails. Also, we do not fix tail behaviour when dealing with fat tail distributions but leave it subject to inference. (AU)

FAPESP's process:	11/22317-0 - Multivariate GARCH models with skewed distributions
Grantee:	Ricardo Sandes Ehlers
Support Opportunities:	Regular Research Grants

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