Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/75131
Type: Artigo de periódico
Title: Stochastic volatility in mean models with scale mixtures of normal distributions and correlated errors: A Bayesian approach
Author: Abanto-Valle, CA
Migon, HS
Lachos, VH
Abstract: A stochastic volatility in mean model with correlated errors using the symmetrical class of scale mixtures of normal distributions is introduced in this article. The scale mixture of normal distributions is an attractive class of symmetric distributions that includes the normal, Student-t, slash and contaminated normal distributions as special cases, providing a robust alternative to estimation in stochastic volatility in mean models in the absence of normality. Using a Bayesian paradigm, an efficient method based on Markov chain Monte Carlo (MCMC) is developed for parameter estimation. The methods developed are applied to analyze daily stock return data from the Sao Paulo Stock, Mercantile & Futures Exchange index (IBOVESPA). The Bayesian predictive information criteria (BPIC) and the logarithm of the marginal likelihood are used as model selection criteria. The results reveal that the stochastic volatility in mean model with correlated errors and slash distribution provides a significant improvement in model fit for the IBOVESPA data over the usual normal model. (C) 2010 Elsevier B.V. All rights reserved.
Subject: Feedback and leverage effect
Markov chain Monte Carlo
Non-Gaussian and nonlinear state-space models
Scale mixture of normal distributions
Stochastic volatility in mean
Country: Holanda
Editor: Elsevier Science Bv
Rights: fechado
Identifier DOI: 10.1016/j.jspi.2010.11.039
Date Issue: 2011
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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