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dc.contributor.authorDominicy, Yves
dc.contributor.authorHeikkilä, Matias
dc.contributor.authorIlmonen, Pauliina
dc.contributor.authorVeredas, David
dc.date.accessioned2019-10-04T07:39:33Z
dc.date.available2019-10-04T07:39:33Z
dc.date.issued2019en_US
dc.identifier.issn0304-4076
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6389
dc.description.abstractDominicy et al. (2017) introduce a family of Hill estimators for elliptically distributed and heavy tailed random vectors. They propose to use the univariate Hill to a norm of order of the data. The norms are homogeneous functions of order one. We show that the family of estimators can be generalized to homogeneous functions of any order and, more importantly, that ellipticity is not required. Only multivariate regular variation is needed, as it is preserved under well-behaved homogeneous functions. This enables us to have flexibility in terms of the estimator and the underlying distribution. Consistency and asymptotic normality are shown, and a Monte Carlo study is conducted to assess the finite sample properties under different asymmetric and heavy tailed multivariate distributions. We illustrate the estimators with an application to 10 years of daily data of paid claims from property insurance policies across 15 regions of Belgium.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectTail Indexen_US
dc.subjectHill Estimatoren_US
dc.subjectExtreme Valueen_US
dc.subjectMultivariate Regular Variationen_US
dc.subjectHomogeneous Functionen_US
dc.titleFlexible multivariate hill estimators (Published Online)en_US
dc.identifier.journalJournal of Econometricsen_US
dc.contributor.departmentBank employee, Luxembourgen_US
dc.contributor.departmentAalto University School of Science, Department of Mathematics and Systems Analysis, Espoo, Finlanden_US
vlerick.knowledgedomainAccounting & Financeen_US
vlerick.typearticleJournal article with impact factoren_US
dc.identifier.vperid181874en_US


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