A Blocking and Regularization Approach to High Dimensional Realized Covariance Estimation

Journal of Applied Econometrics, Forthcoming

30 Pages Posted: 18 Oct 2009 Last revised: 22 Aug 2010

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research; Center for Financial Studies (CFS)

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

Date Written: August 2010

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.

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

JEL Classification: C14, C22

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: https://ssrn.com/abstract=1490414

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research ( email )

Oskar-Morgenstern-Platz 1
Vienna, A-1090
Austria

Center for Financial Studies (CFS) ( email )

Gr├╝neburgplatz 1
Frankfurt am Main, 60323
Germany

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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