Sequential detection of parameter changes in dynamic conditional correlation models

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dc.contributor.authorPape, Katharina
dc.contributor.authorGaleano, Pedro
dc.contributor.authorWied, Dominik
dc.date.accessioned2017-04-06T11:27:08Z
dc.date.available2017-04-06T11:27:08Z
dc.date.issued2017
dc.description.abstractA multivariate monitoring procedure is presented to detect changes in the parameter vector of the dynamic conditional correlation model proposed by Robert Engle in 2002. The benefit of the proposed procedure is that it can be used to detect changes in both the conditional and unconditional variance as well as in the correlation structure of the model. The detector is based on quasi log likelihood scores. More precisely, standardized derivations of quasi log likelihood contributions of points in the monitoring period are evaluated at parameter estimates calculated from a historical period. The null hypothesis of a constant parameter vector is rejected if these standardized terms differ too much from those that were expected under the assumption of a constant parameter vector. Under appropriate assumptions on moments and the structure of the parameter space, limit results are derived both under null hypothesis and alternatives. In a simulation study, size and power properties of the procedure are examined in various scenarios.en
dc.identifier.urihttp://hdl.handle.net/2003/35914
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17937
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;7, 2017en
dc.subjectdynamic conditional correlationen
dc.subjectthreshold functionen
dc.subjectparameter changesen
dc.subjectonline detectionen
dc.subjectmultivariate sequencesen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.subject.rswkMultivariate Analysede
dc.titleSequential detection of parameter changes in dynamic conditional correlation modelsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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