Parametric VaR with goodness-of-fit tests based on EDF statistics for extreme returns

dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:30:52Z
dc.date.accessioned2022-10-05T19:02:06Z
dc.date.available2014-05-27T11:30:52Z
dc.date.available2022-10-05T19:02:06Z
dc.date.created2014-05-27T11:30:52Z
dc.date.issued2013-11-01
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.identifier10.1016/j.mcm.2013.07.002
dc.identifierWOS:000325306700007
dc.identifier2-s2.0-84883557862
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3925769
dc.description.abstractParametric 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.rightsAcesso restrito
dc.sourceScopus
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.typeArtigo


Este ítem pertenece a la siguiente institución