Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/56752
Authors: 
Hautsch, Nikolaus
Kyj, Lada M.
Malec, Peter
Year of Publication: 
2011
Series/Report no.: 
SFB 649 discussion paper 2011-059
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.
Subjects: 
spectral decomposition
mixing frequencies
factor model
blocked realized kernel
covariance prediction
portfolio optimization
JEL: 
G11
G17
C58
C14
C38
Document Type: 
Working Paper

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