A note on nonparametric estimation of bivariate tail dependence

dc.contributor.author	Bücher, Axel
dc.date.accessioned	2012-07-16T16:52:19Z
dc.date.available	2012-07-16T16:52:19Z
dc.date.issued	2012-07-16
dc.description.abstract	Nonparametric estimation of tail dependence can be based on a standardization of the marginals if their cumulative distribution functions are known. In this paper it is shown to be asymptotically more efficient if the additional knowledge of the marginals is ignored and estimators are based on ranks. The discrepancy between the two estimators is shown to be substantial for the popular Clayton model. A brief simulation study indicates that the asymptotic conclusions transfer to finite samples.	en
dc.identifier.uri	http://hdl.handle.net/2003/29515
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-14293
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB 823;27/2012
dc.subject	asymptotic variance	en
dc.subject	nonparametric estimation	en
dc.subject	rank-based inference	en
dc.subject	tail copula	en
dc.subject	tail dependence	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	A note on nonparametric estimation of bivariate tail dependence	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access

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