dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorMoralles, Herick Fernando
dc.creatorRebelatto, Daisy Aparecida do Nascimento
dc.creatorSartoris, Alexandre
dc.date2014-05-27T11:30:52Z
dc.date2016-10-25T18:55:05Z
dc.date2014-05-27T11:30:52Z
dc.date2016-10-25T18:55:05Z
dc.date2013-11-01
dc.date.accessioned2017-04-06T02:42:15Z
dc.date.available2017-04-06T02:42:15Z
dc.identifierMathematical and Computer Modelling, v. 58, n. 9-10, p. 1648-1658, 2013.
dc.identifier0895-7177
dc.identifierhttp://hdl.handle.net/11449/76900
dc.identifierhttp://acervodigital.unesp.br/handle/11449/76900
dc.identifier10.1016/j.mcm.2013.07.002
dc.identifierWOS:000325306700007
dc.identifier2-s2.0-84883557862
dc.identifierhttp://dx.doi.org/10.1016/j.mcm.2013.07.002
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/897581
dc.descriptionParametric VaR (Value-at-Risk) is widely used due to its simplicity and easy calculation. However, the normality assumption, often used in the estimation of the parametric VaR, does not provide satisfactory estimates for risk exposure. Therefore, this study suggests a method for computing the parametric VaR based on goodness-of-fit tests using the empirical distribution function (EDF) for extreme returns, and compares the feasibility of this method for the banking sector in an emerging market and in a developed one. The paper also discusses possible theoretical contributions in related fields like enterprise risk management (ERM). © 2013 Elsevier Ltd.
dc.languageeng
dc.relationMathematical and Computer Modelling
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAnderson-Darling
dc.subjectGoodness-of-fit tests
dc.subjectKolmogorov-Smirnov
dc.subjectParametric Value-at-Risk
dc.subjectTails
dc.subjectGoodness-of-fit test
dc.subjectValue at Risk
dc.subjectRisk management
dc.subjectValue engineering
dc.subjectParameter estimation
dc.titleParametric VaR with goodness-of-fit tests based on EDF statistics for extreme returns
dc.typeOtro


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