Stochastic volatility in mean models with heavy-tailed distributions

dc.creatorAbanto-Valle, CA
dc.creatorMigon, HS
dc.creatorLachos, VH
dc.date2012
dc.dateNOV
dc.date2014-07-31T14:12:08Z
dc.date2015-11-26T16:31:42Z
dc.date2014-07-31T14:12:08Z
dc.date2015-11-26T16:31:42Z
dc.date.accessioned2018-03-28T23:12:52Z
dc.date.available2018-03-28T23:12:52Z
dc.identifierBrazilian Journal Of Probability And Statistics. Brazilian Statistical Association, v. 26, n. 4, n. 402, n. 422, 2012.
dc.identifier0103-0752
dc.identifierWOS:000307147800006
dc.identifier10.1214/11-BJPS169
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/75130
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/75130
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1270287
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionA 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 Sao 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.
dc.description26
dc.description4
dc.description402
dc.description422
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.languageen
dc.publisherBrazilian Statistical Association
dc.publisherSao Paulo
dc.publisherBrasil
dc.relationBrazilian Journal Of Probability And Statistics
dc.relationBraz. J. Probab. Stat.
dc.rightsfechado
dc.sourceWeb of Science
dc.subjectFeedback effect
dc.subjectMarkov chain Monte Carlo
dc.subjectnon-Gaussian and nonlinear state space models
dc.subjectscale mixture of normal distributions
dc.subjectstochastic volatility in mean
dc.subjectLinear Mixed Models
dc.subjectTime-series Models
dc.subjectBayesian-analysis
dc.subjectStock Returns
dc.subjectScale Mixtures
dc.subjectSimulation
dc.subjectInference
dc.subjectSmoother
dc.subjectSampler
dc.subjectMarkets
dc.titleStochastic volatility in mean models with heavy-tailed distributions
dc.typeArtículos de revistas


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