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A Blocking and Regularization Approach to High Dimensional Realized Covariance Estimation

Nikolaus HautschLada M. KyjRoel C. A. Oomen

出版	SFB 649, Economic Risk, 2009
URL	http://books.google.com.hk/books?id=gLdXuwEACAAJ&hl=&source=gbs_api

註釋We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven grouping of assets of similar trading frequency ensures the reduction of data loss due to refresh time sampling. In an extensive simulation study mimicking the empirical features of the S & P 1500 universe we show that the 'RnB' estimator yields efficiency gains and outperforms competing kernel estimators for varying liquidity settings, noise-to-signal ratios, and dimensions. An empirical application of forecasting daily covariances of the S & P 500 index confirms the simulation results. -- covariance estimation ; blocking ; realized kernel ; regularization ; microstructure ; asynchronous trading