Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/39326 
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dc.contributor.authorHautsch, Nikolausen
dc.contributor.authorKyj, Lada M.en
dc.contributor.authorOomen, Roel C.A.en
dc.date.accessioned2009-11-05-
dc.date.accessioned2010-08-26T11:57:25Z-
dc.date.available2010-08-26T11:57:25Z-
dc.date.issued2009-
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
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlinen
dc.relation.ispartofseries|aSFB 649 Discussion Paper |x2009,049en
dc.subject.jelC14en
dc.subject.jelC22en
dc.subject.ddc330en
dc.subject.keywordcovariance estimationen
dc.subject.keywordblockingen
dc.subject.keywordrealized kernelen
dc.subject.keywordregularizationen
dc.subject.keywordmicrostructureen
dc.subject.keywordasynchronous tradingen
dc.subject.stwVarianzanalyseen
dc.subject.stwSchätztheorieen
dc.subject.stwCoreen
dc.subject.stwMultivariate Analyseen
dc.subject.stwTheorieen
dc.subject.stwSchätzungen
dc.subject.stwBörsenkursen
dc.subject.stwWertpapierhandelen
dc.subject.stwAktienmarkten
dc.subject.stwMikrostrukturanalyseen
dc.subject.stwUSAen
dc.titleA blocking and regularization approach to high dimensional realized covariance estimation-
dc.type|aWorking Paperen
dc.identifier.ppn612287025en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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