The merit of high-frequency data in portfolio allocation

Nikolaus Hautsch, Lada M. Kyj and Peter Malec

No 2011-059, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

Abstract: This paper addresses the open debate about the effectiveness and practical relevance of highfrequency (HF) data in portfolio allocation. Our results demonstrate that when used with proper econometric models, HF data offers gains over daily data and more importantly these gains are maintained over longer horizons than previous studies have shown. We propose a Multi-Scale Spectral Components model for forecasting high-dimensional covariance matrices based on realized measures employing HF data. Extensive performance evaluation confirms that the proposed approach dominates prevailing methods and validates the intuition that HF data used properly can translate into better portfolio allocation decisions.

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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Working Paper: The merit of high-frequency data in portfolio allocation (2011)
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