The merit of high-frequency data in portfolio allocation
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.
| Author: | Nikolaus Hautsch, Lada M. Kyj, Peter Malec |
|---|---|
| URN: | urn:nbn:de:hebis:30:3-228716 |
| Series (Serial Number) | CFS working paper series (2011, 24) |
| Document Type: | Working Paper |
| Language: | English |
| Date of Publication (online): | 06.10.2011 |
| Year of first Publication: | 2011 |
| Publishing Institution: | Univ.-Bibliothek Frankfurt am Main |
| Tag: | Blocked Realized Kernel; Covariance Prediction; Factor Model; Mixing Frequencies; Portfolio Optimization; Spectral Decomposition |
| HeBIS PPN: | 279887612 |
| Institutes: | Center for Financial Studies (CFS) |
| Dewey Decimal Classification: | 330 Wirtschaft |
| JEL-Classification: | C14 Semiparametric and Nonparametric Methods |
| C39 Other | |
| C59 Other | |
| G11 Portfolio Choice; Investment Decisions | |
| G17 Financial Forecasting (Updated!) | |
| Licence (German): |  Veröffentlichungsvertrag für Publikationen ohne Print on Demand |
