Bayesian estimation of the gaussian mixture garch model

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dc.contributor.author Ausín, Concepción
dc.contributor.author Galeano, Pedro
dc.date.accessioned 2006-11-09T10:56:58Z
dc.date.available 2006-11-09T10:56:58Z
dc.date.issued 2005-05
dc.identifier.uri http://hdl.handle.net/10016/225
dc.description.abstract In 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.extent 691407 bytes
dc.format.mimetype application/pdf
dc.language.iso eng
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 2005-05
dc.title Bayesian estimation of the gaussian mixture garch model
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.identifier.repec ws053605
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