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dc.contributor.authorBücher, Axel-
dc.contributor.authorSegers, Johan-
dc.date.accessioned2017-06-10T12:40:34Z-
dc.date.available2017-06-10T12:40:34Z-
dc.date.issued2017-
dc.identifier.urihttp://hdl.handle.net/2003/35988-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-18006-
dc.description.abstractThe block maxima method in extreme value theory consists of fitting an extreme value distribution to a sample of block maxima extracted from a time series. Traditionally, the maxima are taken over disjoint blocks of observations. Alternatively, the blocks can be chosen to slide through the observation period, yielding a larger number of overlapping blocks. Inference based on sliding blocks is found to be more efficient than inference based on disjoint blocks. The asymptotic variance of the maximum likelihood estimator of the Fréchet shape parameter is reduced by more than 18%. Interestingly, the amount of the efficiency gain is the same whatever the serial dependence of the underlying time series: as for disjoint blocks, the asymptotic distribution depends on the serial dependence only through the sequence of scaling constants. The findings are illustrated by simulation experiments and are applied to the estimation of high return levels of the daily log-returns of the Standard & Poor's 500 stock market index.en
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;12, 2017en
dc.subjectApéry's constanten
dc.subjectblock maximaen
dc.subjectFréchet distributionen
dc.subjectmaximum likelihood estimatoren
dc.subjectMarshall-Olkin distributionen
dc.subjectPickands dependence functionen
dc.subjectreturn levelen
dc.subject.ddc310-
dc.subject.ddc330-
dc.subject.ddc620-
dc.titleInference for heavy tailed stationary time series based on sliding blocksen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access-
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