A completely automated optimization strategy for global minimum-variance portfolios based on a new test for structural breaks

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dc.contributor.authorBerens, Tobias
dc.contributor.authorWied, Dominik
dc.contributor.authorZiggel, Daniel
dc.date.accessioned2013-05-13T09:03:53Z
dc.date.available2013-05-13T09:03:53Z
dc.date.issued2013-05-13
dc.description.abstractWe present a completely automated optimization strategy which combines the classical Markowitz mean-variance portfolio theory with a recently proposed test for structural breaks in co- variance matrices. With respect to equity portfolios, global minimum-variance optimizations, which base solely on the covariance matrix, yield considerable results in previous studies. However, nancial assets cannot be assumed to have a constant covariance matrix over longer periods of time. Hence, we estimate the covariance matrix of the assets by respecting potential change points. The resulting approach resolves issues like timing or determining a sample for parameter estimation. Moreover, we apply the approach to two datasets and compare the results to relevant benchmark techniques by means of an out-of-sample study. It is shown that the new approach outperforms equally weighted portfolios and plain minimum-variance portfolios on average.en
dc.identifier.urihttp://hdl.handle.net/2003/30310
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-7149
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;19/2013
dc.subjectfluctuation testen
dc.subjectportfolio optimizationen
dc.subjectstructural breaken
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleA completely automated optimization strategy for global minimum-variance portfolios based on a new test for structural breaksen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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