The merit of high-frequency data in portfolio allocation

Nikolaus Hautsch, Lada M. Kyj and Peter Malec

No 2011/24, CFS Working Paper Series from Center for Financial Studies (CFS)

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.

Keywords: Spectral Decomposition; Mixing Frequencies; Factor Model; Blocked Realized Kernel; Covariance Prediction; Portfolio Optimization (search for similar items in EconPapers)
JEL-codes: C14 C38 C58 G11 G17 (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (22)

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Working Paper: The merit of high-frequency data in portfolio allocation (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfswop:201124

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