Nonparametric inference on Lévy measures and copulas

dc.contributor.authorBücher, Axel
dc.contributor.authorVetter, Mathias
dc.date.accessioned2012-05-03T06:29:00Z
dc.date.available2012-05-03T06:29:00Z
dc.date.issued2012-05-03
dc.description.abstractIn this paper nonparametric methods to assess the multivariate Levy measure are introduced. Starting from high-frequency observations of a Levy process X, we construct estimators for its tail integrals and the Pareto Levy copula and prove weak convergence of these estimators in certain function spaces. Given n observations of increments over intervals of length n, the rate of convergence is k1=2 n for kn = nn which is natural concerning inference on the Levy measure. Analytic properties of the Pareto Levy copula which, to the best of our knowledge, have not been mentioned before in the literature are provided as well. We conclude with a short simulation study on the performance of our estimators.en
dc.identifier.urihttp://hdl.handle.net/2003/29430
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-4808
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;16/2012en
dc.subjectcopulaen
dc.subjectLevy copulaen
dc.subjectLevy measureen
dc.subjectLevy processen
dc.subjectnonparametric statisticsen
dc.subjectPareto Levy copulaen
dc.subjectweak convergenceen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleNonparametric inference on Lévy measures and copulasen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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