Open Access
November 2012 Stochastic volatility in mean models with heavy-tailed distributions
Carlos A. Abanto-Valle, Helio S. Migon, Victor H. Lachos
Braz. J. Probab. Stat. 26(4): 402-422 (November 2012). DOI: 10.1214/11-BJPS169


A stochastic volatility in mean (SVM) model using the class of symmetric scale mixtures of normal (SMN) distributions is introduced in this article. The SMN distributions form a class of symmetric thick-tailed distributions that includes the normal one as a special case, providing a robust alternative to estimation in SVM models in the absence of normality. A Bayesian method via Markov-chain Monte Carlo (MCMC) techniques is used to estimate parameters. The deviance information criterion (DIC) and the Bayesian predictive information criteria (BPIC) are calculated to compare the fit of distributions. The method is illustrated by analyzing daily stock return data from the São Paulo Stock, Mercantile & Futures Exchange index (IBOVESPA). According to both model selection criteria as well as out-of-sample forecasting, we found that the SVM model with slash distribution provides a significant improvement in model fit as well as prediction for the IBOVESPA data over the usual normal model.


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Carlos A. Abanto-Valle. Helio S. Migon. Victor H. Lachos. "Stochastic volatility in mean models with heavy-tailed distributions." Braz. J. Probab. Stat. 26 (4) 402 - 422, November 2012.


Published: November 2012
First available in Project Euclid: 3 July 2012

zbMATH: 1319.62205
MathSciNet: MR2949086
Digital Object Identifier: 10.1214/11-BJPS169

Keywords: Feedback effect , Markov chain Monte Carlo , non-Gaussian and nonlinear state space models , scale mixture of normal distributions , stochastic volatility in mean

Rights: Copyright © 2012 Brazilian Statistical Association

Vol.26 • No. 4 • November 2012
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