Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/39326
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dc.contributor.authorHautsch, Nikolausen_US
dc.contributor.authorKyj, Lada M.en_US
dc.contributor.authorOomen, Roel C.A.en_US
dc.date.accessioned2009-11-05en_US
dc.date.accessioned2010-08-26T11:57:25Z-
dc.date.available2010-08-26T11:57:25Z-
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/10419/39326-
dc.description.abstractWe 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.en_US
dc.language.isoengen_US
dc.publisher|aSFB 649, Economic Risk |cBerlinen_US
dc.relation.ispartofseries|aSFB 649 discussion paper |x2009,049en_US
dc.subject.jelC14en_US
dc.subject.jelC22en_US
dc.subject.ddc330en_US
dc.subject.keywordcovariance estimationen_US
dc.subject.keywordblockingen_US
dc.subject.keywordrealized kernelen_US
dc.subject.keywordregularizationen_US
dc.subject.keywordmicrostructureen_US
dc.subject.keywordasynchronous tradingen_US
dc.subject.stwVarianzanalyseen_US
dc.subject.stwSchätztheorieen_US
dc.subject.stwCoreen_US
dc.subject.stwMultivariate Analyseen_US
dc.subject.stwTheorieen_US
dc.subject.stwSchätzungen_US
dc.subject.stwBörsenkursen_US
dc.subject.stwWertpapierhandelen_US
dc.subject.stwAktienmarkten_US
dc.subject.stwMikrostrukturanalyseen_US
dc.subject.stwUSAen_US
dc.titleA blocking and regularization approach to high dimensional realized covariance estimationen_US
dc.type|aWorking Paperen_US
dc.identifier.ppn612287025en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-

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