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The merit of high-frequency data in portfolio allocation

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  • Hautsch, Nikolaus
  • Kyj, Lada M.
  • Malec, Peter

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. --

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Bibliographic Info

Paper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2011/24.

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Date of creation: 2011
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Handle: RePEc:zbw:cfswop:201124

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Keywords: Spectral Decomposition; Mixing Frequencies; Factor Model; Blocked Realized Kernel; Covariance Prediction; Portfolio Optimization;

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