@techreport{Hautsch2009blocking,
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.},
address = {Berlin},
author = {Nikolaus Hautsch and Lada M. Kyj and Roel C.A. Oomen},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {C14; C22; 330; covariance estimation; blocking; realized kernel; regularization; microstructure; asynchronous trading; Varianzanalyse; Sch\"{a}tztheorie; Core; Multivariate Analyse; Theorie; Sch\"{a}tzung; B\"{o}rsenkurs; Wertpapierhandel; Aktienmarkt; Mikrostrukturanalyse; USA},
language = {eng},
number = {2009,049},
publisher = {SFB 649, Economic Risk},
title = {A blocking and regularization approach to high dimensional realized covariance estimation},
type = {SFB 649 discussion paper},
url = {http://hdl.handle.net/10419/39326},
year = {2009}
}
