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Using the multivariate realized kernel estimator of Barndorff\u2010Nielsen <jats:italic>et al.<\/jats:italic> (2010), the covariance matrix is estimated block\u2010wise and then regularized. The performance of the resulting blocking and regularization (\u2018RnB\u2019) estimator is analyzed in an extensive simulation study mimicking the liquidity and market microstructure features of the S&amp;P 1500 universe. The RnB estimator yields efficiency gains for varying liquidity settings, noise\u2010to\u2010signal ratios and dimensions. An empirical application of estimating daily covariances of the S&amp;P 500 index confirms the simulation results. Copyright \u00a9 2010 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jae.1218","type":"journal-article","created":{"date-parts":[[2010,10,22]],"date-time":"2010-10-22T00:14:29Z","timestamp":1287706469000},"page":"625-645","source":"Crossref","is-referenced-by-count":113,"title":["A blocking and regularization approach to high\u2010dimensional realized covariance estimation"],"prefix":"10.1002","volume":"27","author":[{"given":"Nikolaus","family":"Hautsch","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lada M.","family":"Kyj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roel C. 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