A Blocking and Regularization Approach to High Dimensional Realized Covariance Estimation

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