A blocking and regularization approach to high dimensional realized covariance estimation
Nikolaus Hautsch (),
Lada M. Kyj and
Roel Oomen ()
SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649
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 â€™RnBâ€™ 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)
Pages: 34 pages
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mst and nep-ore
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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:hum:wpaper:sfb649dp2009-049
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