Please use this identifier to cite or link to this item:
https://hdl.handle.net/10419/57367
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hautsch, Nikolaus | en |
dc.contributor.author | Kyj, Lada M. | en |
dc.contributor.author | Malec, Peter | en |
dc.date.accessioned | 2011-10-06 | - |
dc.date.accessioned | 2012-04-20T17:09:32Z | - |
dc.date.available | 2012-04-20T17:09:32Z | - |
dc.date.issued | 2011 | - |
dc.identifier.pi | urn:nbn:de:hebis:30:3-228716 | en |
dc.identifier.uri | http://hdl.handle.net/10419/57367 | - |
dc.description.abstract | This 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 |
dc.language.iso | eng | en |
dc.publisher | |aGoethe University Frankfurt, Center for Financial Studies (CFS) |cFrankfurt a. M. | en |
dc.relation.ispartofseries | |aCFS Working Paper |x2011/24 | en |
dc.subject.jel | G11 | en |
dc.subject.jel | G17 | en |
dc.subject.jel | C58 | en |
dc.subject.jel | C14 | en |
dc.subject.jel | C38 | en |
dc.subject.ddc | 330 | en |
dc.subject.keyword | Spectral Decomposition | en |
dc.subject.keyword | Mixing Frequencies | en |
dc.subject.keyword | Factor Model | en |
dc.subject.keyword | Blocked Realized Kernel | en |
dc.subject.keyword | Covariance Prediction | en |
dc.subject.keyword | Portfolio Optimization | en |
dc.subject.stw | Portfolio-Management | en |
dc.subject.stw | Zeitreihenanalyse | en |
dc.subject.stw | Korrelation | en |
dc.subject.stw | Prognoseverfahren | en |
dc.subject.stw | Theorie | en |
dc.title | The merit of high-frequency data in portfolio allocation | - |
dc.type | Working Paper | en |
dc.identifier.ppn | 669404055 | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
dc.identifier.repec | RePEc:zbw:cfswop:201124 | en |
Files in This Item:
Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.