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Fitting heavy Tail distributions with mixture models

datacite.subject.sdg08:Trabalho Digno e Crescimento Económicopt_PT
dc.contributor.authorBasílio, Jorge
dc.contributor.authorOliveira, Amilcar
dc.date.accessioned2023-07-31T11:28:15Z
dc.date.available2023-07-31T11:28:15Z
dc.date.issued2020
dc.description.abstractThe normal probability distribution as assumption for financial returns have been recognized as inappropriate, and a source of inaccurate estimation of Value at Risk. Empirical evidence also have been shown that financial returns shows a more accentuated leptokurtic distribution when compared with a Normal distribution and also skewed. This is usually a cause of underestimated values of VaR, specially when the quantiles are very low. Therefore it is necessary to focus on the tail of the distribution and identify models to fit that behavior. We will highlight the differences between the quality of fitting in the tails of the distribution and the fitting for all the distribution. This work compares and interprets the results obtained by applying mixture models as a method to estimate the behavior on the extremes for heavy tail data distributions. This results will be then used to describe an analytical solution of VaR under mixture models.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.urihttp://hdl.handle.net/10400.2/14666
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherISAST. International Society for the Advancement of Science and Technologypt_PT
dc.relationCentre of Statistics and its Applications
dc.relation.publisherversionhttp://www.smtda.net/images/!SMTDA2020-Proceedings-Final_compressed.pdfpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMixture modelspt_PT
dc.subjectExtreme valuespt_PT
dc.subjectVaRpt_PT
dc.subjectRisk analysespt_PT
dc.titleFitting heavy Tail distributions with mixture modelspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleCentre of Statistics and its Applications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FMAT%2F00006%2F2019/PT
oaire.citation.endPage68pt_PT
oaire.citation.startPage53pt_PT
oaire.citation.titleSMTDA2020. Proceedings of the 6 th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshoppt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameOliveira
person.givenNameAmilcar
person.identifier.ciencia-id7110-61B4-B87F
person.identifier.orcid0000-0001-5500-7742
person.identifier.scopus-author-id55675222550
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication1c873476-22fd-4331-8286-ff5576ac3b0c
relation.isAuthorOfPublication.latestForDiscovery1c873476-22fd-4331-8286-ff5576ac3b0c
relation.isProjectOfPublication4f3cee04-c717-4212-9193-43badeb7c58f
relation.isProjectOfPublication.latestForDiscovery4f3cee04-c717-4212-9193-43badeb7c58f

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