Abstract

http://ssrn.com/abstract=1490414
 
 

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A Blocking and Regularization Approach to High Dimensional Realized Covariance Estimation


Nikolaus Hautsch


University of Vienna - Department of Statistics and Operations Research

Lada M. Kyj


Humboldt University of Berlin; Quantitative Products Laboratory

Roel C. A. Oomen


Deutsche Bank AG (London); London School of Economics & Political Science (LSE) - Department of Statistics

August 2010

Journal of Applied Econometrics, Forthcoming

Abstract:     
We introduce a blocking and regularization approach to estimate high-dimensional covariances using high frequency data. Assets are first grouped according to liquidity. Using the multivariate realized kernel estimator of Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a), the covariance matrix is estimated block-wise and then regularized. The performance of the resulting blocking and regularization ("RnB") estimator is analyzed in an extensive simulation study mimicking the liquidity and market microstructure features of the S&P 1500 universe. The RnB estimator yields efficiency gains for varying liquidity settings, noise-to-signal ratios, and dimensions. An empirical application of estimating daily covariances of the S&P 500 index confirms the simulation results.

Number of Pages in PDF File: 30

Keywords: covariance estimation, blocking, realized kernel, regularization, microstructure noise, asynchronous trading

JEL Classification: C14, C22


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Date posted: October 18, 2009 ; Last revised: August 22, 2010

Suggested Citation

Hautsch, Nikolaus and Kyj, Lada M. and Oomen, Roel C. A., A Blocking and Regularization Approach to High Dimensional Realized Covariance Estimation (August 2010). Journal of Applied Econometrics, Forthcoming. Available at SSRN: http://ssrn.com/abstract=1490414

Contact Information

Nikolaus Hautsch
University of Vienna - Department of Statistics and Operations Research ( email )
Oskar-Morgenstern-Platz 1
Vienna, A-1090
Austria
Lada M. Kyj (Contact Author)
Humboldt University of Berlin ( email )
Unter den Linden 6
Berlin, 10099
Germany
Quantitative Products Laboratory ( email )
Alexanderstrasse 5
Berlin, 10099
Germany
Roel C.A. Oomen
Deutsche Bank AG (London) ( email )
Winchester House
1 Great Winchester Street
London, EC2N 2DB
United Kingdom
London School of Economics & Political Science (LSE) - Department of Statistics ( email )
Houghton Street
London, England WC2A 2AE
United Kingdom
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