The merit of high-frequency data in portfolio allocation

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CFS Working Paper ; No. 2011/24

Classification and Topics

• Classification: Wirtschaft ; Portfolio Choice; Investment Decisions ; Financial Forecasting and Simulation ; Financial Econometrics ; Semiparametric and Nonparametric Methods: General ; Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
• Subject: Spectral Decomposition ; Mixing Frequencies ; Factor Model ; Blocked Realized Kernel ; Covariance Prediction ; Portfolio Optimization ; Portfolio-Management ; Zeitreihenanalyse ; Korrelation ; Prognoseverfahren ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Hautsch, Nikolaus ; Kyj, Lada M. ; Malec, Peter
• Event: Veröffentlichung
• (who): Goethe University Frankfurt, Center for Financial Studies (CFS)
• (where): Frankfurt a. M.
• (when): 2011

Further information

• Handle: http://hdl.handle.net/10419/57367
• URN: urn:nbn:de:hebis:30:3-228716
• Last update: 10.03.2025, 11:44 AM CET