Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/71161
Type: Artigo
Title: Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions
Author: Abanto-Valle, C. A.
Bandyopadhyay, D.
Lachos, V. H.
Enriquez, I.
Abstract: A Bayesian analysis of stochastic volatility (SV) models using the class of symmetric scale mixtures of normal (SMN) distributions is considered. In the face of non-normality, this provides an appealing robust alternative to the routine use of the normal distribution. Specific distributions examined include the normal, student-t, slash and the variance gamma distributions. Using a Bayesian paradigm, an efficient Markov chain Monte Carlo (MCMC) algorithm is introduced for parameter estimation. Moreover, the mixing parameters obtained as a by-product of the scale mixture representation can be used to identify outliers. The methods developed are applied to analyze daily stock returns data on S&P500 index. Bayesian model selection criteria as well as out-of-sample forecasting results reveal that the SV models based on heavy-tailed SMN distributions provide significant improvement in model fit as well as prediction to the S&P500 index data over the usual normal model. (C) 2009 Elsevier B.V. All rights reserved.
A Bayesian analysis of stochastic volatility (SV) models using the class of symmetric scale mixtures of normal (SMN) distributions is considered. In the face of non-normality, this provides an appealing robust alternative to the routine use of the normal
Subject: Métodos MCMC (Estatística)
Misturas de escala (Estatística)
Processo estocástico
Inferência bayesiana
Country: Holanda
Editor: Elsevier
Citation: Computational Statistics & Data Analysis. Elsevier Science Bv, v. 54, n. 12, n. 2883, n. 2898, 2010.
Rights: fechado
Identifier DOI: 10.1016/j.csda.2009.06.011
Address: https://www.sciencedirect.com/science/article/pii/S0167947309002448
Date Issue: 2010
Appears in Collections:IMECC - Artigos e Outros Documentos

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