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Maximum likelihood estimation of score-driven models with dynamic shape parameters : an application to Monte Carlo value-at-risk

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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.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2019-07-22T09:26:43Z
dc.date.available2019-07-22T09:26:43Z
dc.date.issued2019-07-19
dc.description.abstractDynamic conditional score (DCS) models with time-varying shape parameters provide a exible method for volatility measurement. The new models are estimated by using the maximum likelihood (ML) method, conditions of consistency and asymptotic normality of ML are presented, and Monte Carlo simulation experiments are used to study the precision of ML. Daily data from the Standard & Poor's 500 (S&P 500) for the period of 1950 to 2017 are used. The performances of DCS models with constant and dynamic shape parameters are compared. In-sample statistical performance metrics and out-of-sample value-at-risk backtesting support the use of DCS models with dynamic shape.en
dc.description.sponsorshipAstrid Ayala and Szabolcs Blazsek acknowledge funding from the School of Business of Universidad Francisco Marroquín. Alvaro Escribano acknowledges funding from the Spanish Ministry of Economy, Industry and Competitiveness (ECO2015-68715-R, ECO2016- 00105-001), Consolidation Grant (#2006/04046/002), and Maria de Maeztu Grant (MDM 2014-0431).en
dc.identifier.issn2340-5031es
dc.identifier.urihttps://hdl.handle.net/10016/28638
dc.identifier.uxxiDT/0000001717es
dc.language.isoenges
dc.relation.ispartofseriesWorking paper. Economicsen
dc.relation.ispartofseries19-12es
dc.relation.projectIDGobierno de España. ECO2015-68715-Res
dc.relation.projectIDGobierno de España. MDM 2014-0431es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.jelC22es
dc.subject.jelC52es
dc.subject.jelC58es
dc.subject.otherDynamic Conditional Score Modelsen
dc.subject.otherScore-Driven Shape Parametersen
dc.subject.otherValue-At-Risken
dc.subject.otherOutliersen
dc.titleMaximum likelihood estimation of score-driven models with dynamic shape parameters : an application to Monte Carlo value-at-risken
dc.typeworking paper*
dc.type.hasVersionAO*
dspace.entity.typePublication
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