Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/57367
Authors: 
Hautsch, Nikolaus
Kyj, Lada M.
Malec, Peter
Year of Publication: 
2011
Series/Report no.: 
CFS Working Paper 2011/24
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.
Subjects: 
Spectral Decomposition
Mixing Frequencies
Factor Model
Blocked Realized Kernel
Covariance Prediction
Portfolio Optimization
JEL: 
G11
G17
C58
C14
C38
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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