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 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 HautschORCiDGND, Lada M. Kyj, Peter MalecGND
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:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2011/10/06
Tag:Blocked Realized Kernel; Covariance Prediction; Factor Model; Mixing Frequencies; Portfolio Optimization; Spectral Decomposition
Issue:Version September 2011
Page Number:46
HeBIS-PPN:279887612
Institutes:Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
JEL-Classification:C Mathematical and Quantitative Methods / C1 Econometric and Statistical Methods: General / C14 Semiparametric and Nonparametric Methods
C Mathematical and Quantitative Methods / C3 Multiple or Simultaneous Equation Models / C39 Other
C Mathematical and Quantitative Methods / C5 Econometric Modeling / C59 Other
Licence (German):License LogoDeutsches Urheberrecht