Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions

Authors:
C. A. Abanto-Valle;D. Bandyopadhyay;V. H. Lachos;I. Enriquez
Affiliations:
Department of Statistics, Federal University of Rio de Janeiro, Brazil;Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC, USA;Department of Statistics, Campinas State University, Brazil;Department of Statistics, São Paulo State University, Brazil
Venue:
Computational Statistics & Data Analysis
Year:
2010

Citing 3
Cited 2

Simulation of right and left truncated gamma distributions by mixtures

Statistics and Computing
Block sampler and posterior mode estimation for asymmetric stochastic volatility models

Computational Statistics & Data Analysis
Bayesian modeling of financial returns: A relationship between volatility and trading volume

Applied Stochastic Models in Business and Industry

Editorial: Special issue on variable selection and robust procedures

Computational Statistics & Data Analysis
Small area estimation using skew normal models

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

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.