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, w
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
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Metadaten
Author:Nikolaus Hautsch, Lada M. Kyj, Roel C. A. Oomen
URN:urn:nbn:de:hebis:30-72694
Parent Title (German):CFS working paper series ; 2009, 20
Series (Serial Number):CFS working paper series (2009, 20)
Document Type:Working Paper
Language:English
Date of Publication (online):2009/12/02
Year of first Publication:2009
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2009/12/02
Tag:Asynchronous Trading; Blocking ; Covariance Estimation ; Microstructure ; Realized Kernel ; Regularization
HeBIS PPN:220153930
Institutes:Center for Financial Studies (CFS)
Dewey Decimal Classification:330 Wirtschaft
JEL-Classification:C14 Semiparametric and Nonparametric Methods
C22 Time-Series Models; Dynamic Quantile Regressions (Updated!)
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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