Bayesian multivariate GARCH models with dynamic correlations and asymmetric error distributions

Autor(es)

Fioruci, Jose A. ^[1] ; Ehlers, Ricardo S. ^[1] ; Andrade Filho, Marinho G. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Dept Appl Math & Stat, Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Journal of Applied Statistics; v. 41, n. 2, p. 320-331, FEB 1 2014.
• Citações Web of Science: 3

Resumo

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)

• Processo FAPESP: 11/22317-0 - Modelos GARCH multivariados com distribuições assimétricas
• Beneficiário: Ricardo Sandes Ehlers
• Modalidade de apoio: Auxílio à Pesquisa - Regular