Relatório de pesquisa 27/08
Robust Bayesian Analysis of Heavy-tailed Stochastic Volatility Models using Scale Mixtures of Normal Distributions, C. A. Abanto-Valle, D. Bandyopadhyay, V. H. Lachos, and I. Enriquez, submitted Nov. 28.
This paper consider a Bayesian analysis of stochastic volatility
models using a class of symmetric normal scale mixtures, which provides
an appealing robust alternative to the routine use of the normal
distribution in this type of models. Specific distributions examined
include the normal, the Student-t, the slash and the variance gamma
distribution which are obtained as a sub-class of our proposed class of
models. Under a Bayesian paradigm, we explore an efficient Markov chain
Monte Carlo (MCMC) algorithm for parameter estimation in this model.
Moreover, the mixing parameters obtained as a by-product of the scale
mixture representation can be used to identify possible outliers. The
methods developed are applied to analyze daily stock returns data on
S\&P500 index. We conclude that our proposed rich class of normal
scale mixture models provides an interesting robust alternative to the
traditional normality assumptions often used to model thick-tailed
stochastic volatility data.
Mathematics Subject Classifications
Keywords: EM algorithm; Scale mixtures of normal distribution; Mahalanobis distance; Measurement error models
of the file:
rp27-08.pdf.gz (gzipped PDF)
November 28, 2008
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