Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/57367 
Erscheinungsjahr: 
2011
Schriftenreihe/Nr.: 
CFS Working Paper No. 2011/24
Verlag: 
Goethe University Frankfurt, Center for Financial Studies (CFS), Frankfurt a. M.
Zusammenfassung: 
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.
Schlagwörter: 
Spectral Decomposition
Mixing Frequencies
Factor Model
Blocked Realized Kernel
Covariance Prediction
Portfolio Optimization
JEL: 
G11
G17
C58
C14
C38
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
845.11 kB





Publikationen in EconStor sind urheberrechtlich geschützt.