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Bayesian estimation of the gaussian mixture garch model

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorAusín Olivera, María Concepción
dc.contributor.authorGaleano, Pedro
dc.date.accessioned2006-11-09T10:56:58Z
dc.date.available2006-11-09T10:56:58Z
dc.date.issued2005-05
dc.description.abstractIn this paper, we perform Bayesian inference and prediction for a GARCH model where the innovations are assumed to follow a mixture of two Gaussian distributions. This 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. The method is illustrated using the Swiss Market Index.
dc.format.extent691407 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.repecws053605
dc.identifier.urihttps://hdl.handle.net/10016/225
dc.language.isoeng
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries2005-05
dc.rights.accessRightsopen access
dc.subject.ecienciaEstadística
dc.titleBayesian estimation of the gaussian mixture garch model
dc.typeworking paper*
dspace.entity.typePublication
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