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dc.contributor.authorSivanesan, Sivanja-
dc.contributor.authorDette, Holger-
dc.contributor.authorZiggel, Daniel-
dc.date.accessioned2021-08-16T13:39:02Z-
dc.date.available2021-08-16T13:39:02Z-
dc.date.issued2021-
dc.identifier.urihttp://hdl.handle.net/2003/40472-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22347-
dc.description.abstractIn this report we investigate the finite sample properties of a new online monitoring scheme which was recently introduced by Gösmann et al. (2020) by means of a simulation study and a real data example. We also develop an algorithm which can be used in an active risk management. We start with an introduction in the basic notation and an explanation of the monitoring procedure, continue with an extensive simulation study to provide recommendations for the choice of several tuning parameters. Finally we present some illustration analyzing the Standard & Poor’s 500, MSCI World and MSCI Emerging Markets indices.en
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;18/2021-
dc.subject.ddc310-
dc.subject.ddc330-
dc.subject.ddc620-
dc.titleSome practical aspects of sequential change point detectionen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dc.subject.rswkRisk Managementde
dcterms.accessRightsopen access-
eldorado.secondarypublicationfalsede
Appears in Collections:Sonderforschungsbereich (SFB) 823

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