TY  - UNPD
A1  - Hautsch, Nikolaus
A1  - Kyj, Lada M.
A1  - Malec, Peter
T1  - The merit of high-frequency data in portfolio allocation
T2  - Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2011,24
N2  - 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.
T3  - CFS working paper series - 2011, 24 
KW  - Spectral Decomposition
KW  - Mixing Frequencies
KW  - Factor Model
KW  - Blocked Realized Kernel
KW  - Covariance Prediction
KW  - Portfolio Optimization
Y1  - 2011
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22871
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-228716
IS  - Version September 2011
ER  -