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Sonderforschungsbereich 649: Ökonomisches Risiko, Humboldt-Universität Berlin >
SFB 649 Discussion Papers, HU Berlin >
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http://hdl.handle.net/10419/39326
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| Title: | | A blocking and regularization approach to high dimensional realized covariance estimation  |
| Authors: | | Hautsch, Nikolaus Kyj, Lada M. Oomen, Roel C.A. |
| Issue Date: | | 2009 |
| Series/Report no.: | | SFB 649 discussion paper 2009,049 |
| 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. |
| Subjects: | | covariance estimation blocking realized kernel regularization microstructure asynchronous trading |
| JEL: | | C14 C22 |
| Document Type: | | Working Paper |
| Appears in Collections: | | SFB 649 Discussion Papers, HU Berlin
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