TY  - UNPD
A1  - Hautsch, Nikolaus
A1  - Kyj, Lada M.
A1  - Oomen, Roel C. A.
T1  - A blocking and regularization approach to high dimensional realized covariance estimation
T2  - Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2009,20
N2  - 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.
T3  - CFS working paper series - 2009, 20 
KW  - Covariance Estimation
KW  - Blocking
KW  - Realized Kernel
KW  - Regularization
KW  - Microstructure
KW  - Asynchronous Trading
KW  - Kovarianzanalyse
KW  - Schätzfunktion
Y1  - 2009
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7286
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30-72694
ER  -