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  • Search: subject:"Covariance Prediction"
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Year of publication
Subject
All
blocked realized kernel 5 covariance prediction 5 portfolio optimization 5 spectral decomposition 5 Korrelation 3 Portfolio-Management 3 Prognoseverfahren 3 Theorie 3 Zeitreihenanalyse 3 regularization 3 Blocked Realized Kernel 2 Covariance Prediction 2 Factor Model 2 Mixing Frequencies 2 Portfolio Optimization 2 Spectral Decomposition 2 factor model 2 mixing frequencies 2 Analysis of variance 1 Capital income 1 Correlation 1 Decomposition method 1 Dekompositionsverfahren 1 Forecasting model 1 Kapitaleinkommen 1 Portfolio selection 1 Theory 1 Time series analysis 1 Varianzanalyse 1 Volatility 1 Volatilität 1
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Online availability
All
Free 7
Type of publication
All
Book / Working Paper 7
Type of publication (narrower categories)
All
Working Paper 4 Arbeitspapier 1 Graue Literatur 1 Non-commercial literature 1
Language
All
English 4 Undetermined 3
Author
All
Hautsch, Nikolaus 7 Malec, Peter 7 Kyj, Lada M. 5 Kyj, Lada. M. 2
Institution
All
Sonderforschungsbereich 649: Ökonomisches Risiko, Wirtschaftswissenschaftliche Fakultät 2 Center for Financial Studies 1
Published in...
All
SFB 649 Discussion Paper 2 SFB 649 Discussion Papers 2 CFS Working Paper 1 CFS Working Paper Series 1 SFB 649 discussion paper 1
Source
All
EconStor 3 RePEc 3 ECONIS (ZBW) 1
Showing 1 - 7 of 7
Cover Image
Do high-frequency data improve high-dimensional portfolio allocations?
Hautsch, Nikolaus; Kyj, Lada. M.; Malec, Peter - 2013
This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. We consider the problem of constructing global minimum variance portfolios based on the constituents of the S&P 500 over a four-year period covering the 2008 financial...
Persistent link: https://www.econbiz.de/10010318770
Saved in:
Cover Image
Do High-Frequency Data Improve High-Dimensional Portfolio Allocations?
Hautsch, Nikolaus; Kyj, Lada M.; Malec, Peter - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 2013
This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. We consider the problem of constructing global minimum variance portfolios based on the constituents of the S&P 500 over a four-year period covering the 2008 financial...
Persistent link: https://www.econbiz.de/10010617848
Saved in:
Cover Image
Do high-frequency data improve high-dimensional portfolio allocations?
Hautsch, Nikolaus; Kyj, Lada. M.; Malec, Peter - 2013 - First version: September 2011, This version: February 2013
This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. We consider the problem of constructing global minimum variance portfolios based on the constituents of the S&P 500 over a four-year period covering the 2008 financial...
Persistent link: https://www.econbiz.de/10009714536
Saved in:
Cover Image
The merit of high-frequency data in portfolio allocation
Hautsch, Nikolaus; Kyj, Lada M.; Malec, Peter - 2011
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...
Persistent link: https://www.econbiz.de/10010308574
Saved in:
Cover Image
The merit of high-frequency data in portfolio allocation
Hautsch, Nikolaus; Kyj, Lada M.; Malec, Peter - 2011
This paper addresses the open debate about the effectiveness and practical relevance of highfrequency (HF) data in portfolio allocation. Our results demonstrate that when used with proper econometric models, HF data offers gains over daily data and more importantly these gains are maintained...
Persistent link: https://www.econbiz.de/10010281594
Saved in:
Cover Image
The merit of high-frequency data in portfolio allocation
Hautsch, Nikolaus; Kyj, Lada M.; Malec, Peter - Center for Financial Studies - 2011
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...
Persistent link: https://www.econbiz.de/10010958793
Saved in:
Cover Image
The Merit of High-Frequency Data in Portfolio Allocation
Hautsch, Nikolaus; Kyj, Lada M.; Malec, Peter - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 2011
This paper addresses the open debate about the effectiveness and practical relevance of highfrequency (HF) data in portfolio allocation. Our results demonstrate that when used with proper econometric models, HF data offers gains over daily data and more importantly these gains are maintained...
Persistent link: https://www.econbiz.de/10010587713
Saved in:
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