The merit of high-frequency data in portfolio allocation

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 real
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.
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Metadaten
Author:Nikolaus Hautsch, Lada M. Kyj, Peter Malec
URN:urn:nbn:de:hebis:30:3-228716
Parent Title (German):Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2011,24
Series (Serial Number):CFS working paper series (2011, 24)
Document Type:Working Paper
Language:English
Year of Completion:2011
Year of first Publication:2011
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2011/10/06
Tag:Blocked Realized Kernel; Covariance Prediction; Factor Model; Mixing Frequencies; Portfolio Optimization; Spectral Decomposition
Issue:Version September 2011
Pagenumber:46
HeBIS PPN:279887612
Institutes:Center for Financial Studies (CFS)
Dewey Decimal Classification:330 Wirtschaft
JEL-Classification:C14 Semiparametric and Nonparametric Methods
C39 Other
C59 Other
G11 Portfolio Choice; Investment Decisions
G17 Financial Forecasting (Updated!)
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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