Testing for structural breaks in correlation

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dc.contributor.authorBerens, Tobias
dc.contributor.authorWeiß, Gregor N.F.
dc.contributor.authorWied, Dominik
dc.date.accessioned2013-05-22T09:24:27Z
dc.date.available2013-05-22T09:24:27Z
dc.date.issued2013-05-22
dc.description.abstractIn this paper, we compare the Constant Conditional Correlation (CCC) model to its dynamic counterpart, the Dynamic Conditional Correlation (DCC) model with respect to its accuracy for forecasting the Value-at-Risk of financial portfolios. Additionally, we modify these benchmark models by combining them with a pairwise test for constant correlations, a test for a constant correlation matrix, and a test for a constant covariance matrix. In an empirical horse race of these models based on five- and ten-dimensional portfolios, our study shows that the plain CCC- and DCC-GARCH models are outperformed in several settings by the approaches modified by tests for structural breaks in asset correlations and covariances.en
dc.identifier.urihttp://hdl.handle.net/2003/30330
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-5396
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;21/2013en
dc.subjectCCC-GARCHen
dc.subjectDCC-GARCHen
dc.subjectestimation windowen
dc.subjectstructural breaksen
dc.subjectVaR-forecasten
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleTesting for structural breaks in correlationen
dc.title.alternativeDoes it improve Value-at-Risk forecasting?en
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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Sonderforschungsbereich (SFB) 823