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dc.contributor.authorHautsch, Nikolausen_US
dc.contributor.authorKyj, Lada M.en_US
dc.contributor.authorMalec, Peteren_US
dc.date.accessioned2011-10-06en_US
dc.date.accessioned2012-04-20T17:09:32Z-
dc.date.available2012-04-20T17:09:32Z-
dc.date.issued2011en_US
dc.identifier.piurn:nbn:de:hebis:30:3-228716-
dc.identifier.urihttp://hdl.handle.net/10419/57367-
dc.description.abstractThis paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a multi-scale spectral decomposition where volatilities, correlation eigenvalues and eigenvectors evolve on different frequencies. In an extensive out-of-sample forecasting study, we show that the proposed approach yields less risky and more diversified portfolio allocations as prevailing methods employing daily data. These performance gains hold over longer horizons than previous studies have shown.en_US
dc.language.isoengen_US
dc.publisherCenter for Financial Studies Frankfurt, Mainen_US
dc.relation.ispartofseriesCFS Working Paper 2011/24en_US
dc.subject.jelG11en_US
dc.subject.jelG17en_US
dc.subject.jelC58en_US
dc.subject.jelC14en_US
dc.subject.jelC38en_US
dc.subject.ddc330en_US
dc.subject.keywordSpectral Decompositionen_US
dc.subject.keywordMixing Frequenciesen_US
dc.subject.keywordFactor Modelen_US
dc.subject.keywordBlocked Realized Kernelen_US
dc.subject.keywordCovariance Predictionen_US
dc.subject.keywordPortfolio Optimizationen_US
dc.subject.stwPortfolio-Managementen_US
dc.subject.stwZeitreihenanalyseen_US
dc.subject.stwKorrelationen_US
dc.subject.stwPrognoseverfahrenen_US
dc.subject.stwTheorieen_US
dc.titleThe merit of high-frequency data in portfolio allocationen_US
dc.typeWorking Paperen_US
dc.identifier.ppn669404055en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
dc.identifier.repecRePEc:zbw:cfswop:201124-
Appears in Collections:CFS Working Paper Series, Universität Frankfurt a. M.

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