A Blocking and Regularization Approach to High Dimensional Realized Covariance Estimation
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
Journal of Applied Econometrics, Forthcoming
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
Date posted: October 18, 2009 ; Last revised: August 22, 2010
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