Publication: Bayesian estimation of the gaussian mixture garch model
dc.affiliation.dpto | UC3M. Departamento de Estadística | es |
dc.contributor.author | Ausín Olivera, María 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.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.identifier.repec | ws053605 | |
dc.identifier.uri | https://hdl.handle.net/10016/225 | |
dc.language.iso | eng | |
dc.language.iso | eng | |
dc.relation.ispartofseries | UC3M Working Papers. Statistics and Econometrics | |
dc.relation.ispartofseries | 2005-05 | |
dc.rights.accessRights | open access | |
dc.subject.eciencia | Estadística | |
dc.title | Bayesian estimation of the gaussian mixture garch model | |
dc.type | working paper | * |
dspace.entity.type | Publication |
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