Abstract
SUMMARYWe 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 et al. (2010), 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. Copyright © 2010 John Wiley & Sons, Ltd.
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