Goethe-Universität Frankfurt am Main >
Center for Financial Studies (CFS), Universität Frankfurt a. M. >
CFS Working Paper Series, Universität Frankfurt a. M. >
Please use this identifier to cite or link to this item:
| || |
|Title:||The merit of high-frequency data in portfolio allocation |
Kyj, Lada M.
|Issue Date:||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.|
Blocked Realized Kernel
|Document Type:||Working Paper|
|Appears in Collections:||CFS Working Paper Series, Universität Frankfurt a. M.|
Download bibliographical data as:
Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.