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Anticipating extreme losses using score-driven shape filters

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dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorAyala, Astrid
dc.contributor.authorBlazsek, Szabolcs Istvan
dc.contributor.authorEscribano, Álvaro
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2023-09-27T14:09:25Z
dc.date.available2023-09-27T14:09:25Z
dc.date.issued2022-01-01
dc.description.abstractWe suggest a new value-at-risk (VaR) framework using EGARCH (exponential generalized autoregressive conditional heteroskedasticity) models with score-driven expected return, scale, and shape filters. We use the EGB2 (exponential generalized beta of the second kind), NIG (normal-inverse Gaussian), and Skew-Gen-t (skewed generalized-t) distributions, for which the score-driven shape parameters drive the skewness, tail shape, and peakedness of the distribution. We use daily data on the Standard & Poor"s 500 (S&P 500) index for the period of February 1990 to October 2021. For all distributions, likelihood-ratio (LR) tests indicate that several EGARCH models with dynamic shape are superior to the EGARCH models with constant shape. We compare the realized volatility with the conditional volatility estimates, and we find two Skew-Gen-t specifications with dynamic shape, which are superior to the Skew-Gen-t specification with constant shape. The shape parameter dynamics are associated with important events that affected the stock market in the United States (US). VaR backtesting is performed for the dot.com boom (January 1997 to October 2020), the 2008 US Financial Crisis (October 2007 to March 2009), and the coronavirus disease (COVID-19) pandemic (January 2020 to October 2021). We show that the use of the dynamic shape parameters improves the VaR measurementsen
dc.description.sponsorshipAyala and Blazsek acknowledge funding from Universidad Francisco Marroquín. 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.bibliographicCitationAyala, A., Blazsek, S. & Escribano, A. (2022). Anticipating extreme losses using score-driven shape filters. Studies in Nonlinear Dynamics & Econometrics.en
dc.identifier.doihttps://doi.org/10.1515/snde-2021-0102
dc.identifier.issn1081-1826
dc.identifier.publicationfirstpage1es
dc.identifier.publicationlastpage36es
dc.identifier.publicationtitleSTUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICSen
dc.identifier.publicationvolume2022es
dc.identifier.urihttps://hdl.handle.net/10016/38456
dc.identifier.uxxiAR/0000032743
dc.language.isoenges
dc.publisherDe Gruyteren
dc.relation.projectIDGobierno de España. ECO2015-68715-Res
dc.relation.projectIDGobierno de España. ECO2016-00105-001es
dc.relation.projectIDGobierno de España. MDM 2014-0431es
dc.rights© 2022, The author(s)es
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaEconomíaes
dc.subject.ecienciaEstadísticaes
dc.subject.jelC22
dc.subject.jelC52
dc.subject.jelC58.
dc.subject.otherDynamic conditional score (DCS)en
dc.subject.otherGeneralized autoregressive score (GAS)en
dc.subject.otherScore-driven hhape parametersen
dc.subject.otherValue-at-Rrsk (VAR)en
dc.subject.otherVar backtestingen
dc.titleAnticipating extreme losses using score-driven shape filtersen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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