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dc.contributor.authorAusín, M. Concepción
dc.contributor.authorGaleano, Pedro
dc.date.accessioned2007-07-05T15:06:53Z
dc.date.available2007-07-05T15:06:53Z
dc.date.issued2007
dc.identifier.citationComputational Statistics & Data Analysis, 2007, 51, p. 2636 – 2652es_ES
dc.identifier.issn0167-9473
dc.identifier.urihttp://hdl.handle.net/2183/867
dc.description.abstractBayesian inference and prediction for a generalized autoregressive conditional heteroskedastic (GARCH) model where the innovations are assumed to follow a mixture of two Gaussian distributions is performed. The mixture GARCH model can capture the patterns usually exhibited by many financial time series such as volatility clustering, large kurtosis and extreme observations. A Griddy–Gibbs sampler implementation is proposed for parameter estimation and volatility prediction. Bayesian prediction of the Value at Risk is also addressed providing point estimates and predictive intervals. The method is illustrated using the Swiss Market Index.es_ES
dc.format.mimetypeapplication/pdf
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.urihttp://www.elsevier.com/locate/csdaes_ES
dc.subjectBayesian inferencees_ES
dc.subjectGARCH modelses_ES
dc.subjectGriddy–Gibbs sampleres_ES
dc.subjectMixtures modelses_ES
dc.subjectValue at riskes_ES
dc.titleBayesian estimation of the Gaussian mixture GARCH modeles_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES


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