The Merit of High-Frequency Data in Portfolio Allocation
Humboldt-Universität zu Berlin; CASE - Center for Applied Statistics and Economics; CFS
Lada M. Kyj
Humboldt University of Berlin; Quantitative Products Laboratory
Humboldt Universität zu Berlin
September 12, 2011
This paper addresses the open debate about the effectiveness and practical relevance of high-frequency (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.
Number of Pages in PDF File: 43
Keywords: spectral decomposition, mixing frequencies, factor model, blocked realized kernel, covariance prediction, portfolio optimization
JEL Classification: G11, G17, C58, C14, C38working papers series
Date posted: September 12, 2011
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