Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions
Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
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Data(s) |
20/05/2014
20/05/2014
01/12/2010
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Resumo |
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 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. |
Formato |
2883-2898 |
Identificador |
http://dx.doi.org/10.1016/j.csda.2009.06.011 Computational Statistics & Data Analysis. Amsterdam: Elsevier B.V., v. 54, n. 12, p. 2883-2898, 2010. 0167-9473 http://hdl.handle.net/11449/39915 10.1016/j.csda.2009.06.011 WOS:000281333900002 |
Idioma(s) |
eng |
Publicador |
Elsevier B.V. |
Relação |
Computational Statistics & Data Analysis |
Direitos |
closedAccess |
Tipo |
info:eu-repo/semantics/conferenceObject |