A blocking and regularization approach to high dimensional realized covariance estimation

Nikolaus Hautsch, Lada M. Kyj and Nikolaus Hautsch
Authors registered in the RePEc Author Service: Nikolaus Hautsch

No 2009/20, CFS Working Paper Series from Center for Financial Studies (CFS)

Abstract: 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.

Keywords: Covariance Estimation; Blocking; Realized Kernel; Regularization; Microstructure; Asynchronous Trading (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (7)

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Related works:
Journal Article: A blocking and regularization approach to high‐dimensional realized covariance estimation (2012)
Working Paper: A blocking and regularization approach to high dimensional realized covariance estimation (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfswop:200920

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