Brazilian Journal of Probability and Statistics

Stochastic volatility in mean models with heavy-tailed distributions

Carlos A. Abanto-Valle, Helio S. Migon, and Victor H. Lachos

Full-text: Open access

Abstract

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.

Article information

Source
Braz. J. Probab. Stat., Volume 26, Number 4 (2012), 402-422.

Dates
First available in Project Euclid: 3 July 2012

Permanent link to this document
https://projecteuclid.org/euclid.bjps/1341320250

Digital Object Identifier
doi:10.1214/11-BJPS169

Mathematical Reviews number (MathSciNet)
MR2949086

Zentralblatt MATH identifier
1319.62205

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

Citation

Abanto-Valle, Carlos A.; Migon, Helio S.; Lachos, Victor H. Stochastic volatility in mean models with heavy-tailed distributions. Braz. J. Probab. Stat. 26 (2012), no. 4, 402--422. doi:10.1214/11-BJPS169. https://projecteuclid.org/euclid.bjps/1341320250


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