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Dynamic conditional score models with time-varying location, scale and shape parameters

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dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorAyala, Astrid
dc.contributor.authorBlazsek, Szabolcs
dc.contributor.authorEscribano, Álvaro
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Economíaes
dc.date.accessioned2017-07-26T12:45:07Z
dc.date.available2017-07-26T12:45:07Z
dc.date.issued2017-07-01
dc.description.abstractWe introduce new dynamic conditional score (DCS) models with time-varyinglocation, scale and shape parameters. For these models, we use the Student's-t, GED(general error distribution), Gen-t (generalized-t), Skew-Gen-t (skewed generalized-t),EGB2 (exponential generalized beta of the second kind) and NIG (normal-inverseGaussian) distributions. We show that the maximum likelihood (ML) estimates of thenew DCS models are consistent and asymptotically Gaussian. As an illustration, weuse daily log-return time series data from the S&P 500 index for period 1950 to 2016.We find that, with respect to goodness-of-fit and predictive performance, the DCSmodels with dynamic shape are superior to the DCS models with constant shape andthe benchmark AR-t-GARCH model.en
dc.format.mimetypeapplication/pdf
dc.identifier.issn2340-5031es
dc.identifier.urihttps://hdl.handle.net/10016/25043
dc.identifier.uxxiDT/0000001577es
dc.language.isoenges
dc.relation.ispartofseriesUC3M Working papers. Economicsen
dc.relation.ispartofseries17-08
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.jelC22
dc.subject.jelC52
dc.subject.jelC58
dc.subject.otherDynamic conditional score modelsen
dc.subject.otherScore-driven shape parametersen
dc.titleDynamic conditional score models with time-varying location, scale and shape parametersen
dc.typeworking paper*
dspace.entity.typePublication
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