Please use this identifier to cite or link to this item:
Type: Artigo de periódico
Title: Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions
Author: Abanto-Valle, CA
Bandyopadhyay, D
Lachos, VH
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.
Country: Holanda
Editor: Elsevier Science Bv
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
Date Issue: 2010
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
WOS000281333900002.pdf915.03 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.