<<A>> blocking and regularization approach to high dimensional realized covariance estimation
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 gro...
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Berlin
SFB 649, Economic Risk
2009
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| Schriftenreihe: | SFB 649 discussion paper
2009,049 |
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Tag hinzufügen | Zusammenfassung: | 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 |
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| Beschreibung: | 29 S. graph. Darst. |