Arbeitspapier
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: CFS Working Paper ; No. 2011/24
- Klassifikation
-
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
- Thema
-
Spectral Decomposition
Mixing Frequencies
Factor Model
Blocked Realized Kernel
Covariance Prediction
Portfolio Optimization
Portfolio-Management
Zeitreihenanalyse
Korrelation
Prognoseverfahren
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Hautsch, Nikolaus
Kyj, Lada M.
Malec, Peter
- Ereignis
-
Veröffentlichung
- (wer)
-
Goethe University Frankfurt, Center for Financial Studies (CFS)
- (wo)
-
Frankfurt a. M.
- (wann)
-
2011
- Handle
- URN
-
urn:nbn:de:hebis:30:3-228716
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Hautsch, Nikolaus
- Kyj, Lada M.
- Malec, Peter
- Goethe University Frankfurt, Center for Financial Studies (CFS)
Entstanden
- 2011
Ähnliche Objekte (12)
The merit of high-frequency data in portfolio allocation
Large-scale portfolio allocation under transaction costs and model uncertainty
Capturing common components in high-frequency financial time series: a multivariate stochastic multiplicative error model
Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model
Do high-frequency data improve high-dimensional portfolio allocations?
Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information
Modelling high-frequency volatility and liquidity using multiplicative error models
Quantifying high-frequency market reactions to real-time news sentiment announcements
Quantifying high-frequency market reactions to real-time news sentiment announcements
Measuring and modeling risk using high-frequency data
Testing multiplicative error models using conditional moment tests
Analyzing the Time between Trades with a Gamma Compounded Hazard Model. An Application to LIFFE Bund Future Transactions
The merit of high-frequency data in portfolio allocation
Large-scale portfolio allocation under transaction costs and model uncertainty
Capturing common components in high-frequency financial time series: a multivariate stochastic multiplicative error model
Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model
Do high-frequency data improve high-dimensional portfolio allocations?
Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information
Modelling high-frequency volatility and liquidity using multiplicative error models
Quantifying high-frequency market reactions to real-time news sentiment announcements
Quantifying high-frequency market reactions to real-time news sentiment announcements
Measuring and modeling risk using high-frequency data
Testing multiplicative error models using conditional moment tests
Analyzing the Time between Trades with a Gamma Compounded Hazard Model. An Application to LIFFE Bund Future Transactions
The merit of high-frequency data in portfolio allocation
Large-scale portfolio allocation under transaction costs and model uncertainty
Capturing common components in high-frequency financial time series: a multivariate stochastic multiplicative error model
Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model
Do high-frequency data improve high-dimensional portfolio allocations?
Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information
Modelling high-frequency volatility and liquidity using multiplicative error models
Quantifying high-frequency market reactions to real-time news sentiment announcements
Quantifying high-frequency market reactions to real-time news sentiment announcements
Measuring and modeling risk using high-frequency data
Testing multiplicative error models using conditional moment tests