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Prediction accuracy of bivariate score-driven risk premium and volatility filters: an illustration for the Dow Jones

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dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorBlazsek, Szabolcs
dc.contributor.authorEscribano, Álvaro
dc.contributor.authorLicht, Adrian
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Economíaes
dc.date.accessioned2020-11-05T15:45:33Z
dc.date.available2020-11-05T15:45:33Z
dc.date.issued2020-11-05es
dc.description.abstractIn this paper, we introduce Beta-t-QVAR (quasi-vector autoregression) for the joint modelling of score-driven location and scale. Asymptotic theory of the maximum likelihood (ML) estimatoris presented, and sufficient conditions of consistency and asymptotic normality of ML are proven. Forthe joint score-driven modelling of risk premium and volatility, Dow Jones Industrial Average (DJIA)data are used in an empirical illustration. Prediction accuracy of Beta-t-QVAR is superior to theprediction accuracies of Beta-t-EGARCH (exponential generalized AR conditional heteroscedasticity),A-PARCH (asymmetric power ARCH), and GARCH (generalized ARCH). The empirical results motivate the use of Beta-t-QVAR for the valuation of DJIA options.en
dc.identifier.issn2340-5031es
dc.identifier.urihttps://hdl.handle.net/10016/31339
dc.identifier.uxxiDT/0000001850es
dc.language.isoenges
dc.relation.ispartofseriesWorking paper. Economicsen
dc.relation.ispartofseries20-10es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.jelC22
dc.subject.jelC58
dc.subject.otherVolatilityen
dc.subject.otherRisk Premiumen
dc.subject.otherDynamic Conditional Scoreen
dc.subject.otherGeneralized Autoregressive Scoreen
dc.titlePrediction accuracy of bivariate score-driven risk premium and volatility filters: an illustration for the Dow Jonesen
dc.typeworking paper*
dspace.entity.typePublication
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