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The Merit of High-Frequency Data in Portfolio Allocation

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  • Nikolaus Hautsch
  • Lada M. Kyj
  • Peter Malec

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.

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File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2011-059.pdf
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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2011-059.

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Length: 47 pages
Date of creation: Sep 2011
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2011-059

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Keywords: spectral decomposition; mixing frequencies; factor model; blocked realized kernel; covariance prediction; portfolio optimization;

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