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2013-03-08Buch DOI: 10.18452/4454
Do High-Frequency Data Improve High-Dimensional Portfolio Allocations?
dc.contributor.authorHautsch, Nikolaus
dc.contributor.authorKyj, Lada M.
dc.contributor.authorMalec, Peter
dc.date.accessioned2017-06-16T00:49:15Z
dc.date.available2017-06-16T00:49:15Z
dc.date.created2013-03-18
dc.date.issued2013-03-08
dc.date.submitted2013-03-08
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/5106
dc.description.abstractThis paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. We consider the problem of constructing global minimum variance portfolios based on the constituents of the S&P 500 over a four-year period covering the 2008 financial crisis. HF-based covariance matrix predictions are obtained by applying a blocked realized kernel estimator, different smoothing windows, various regularization methods and two forecasting models. We show that HF-based predictions yield a significantly lower portfolio volatility than methods employing daily returns. Particularly during the volatile crisis period, these performance gains hold over longer horizons than previous studies have shown and translate into substantial utility gains from the perspective of an investor with pronounced risk aversion.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectspectral decompositioneng
dc.subjectblocked realized kerneleng
dc.subjectcovariance predictioneng
dc.subjectportfolio optimizationeng
dc.subjectregularizationeng
dc.subject.ddc310 Statistik
dc.subject.ddc330 Wirtschaft
dc.titleDo High-Frequency Data Improve High-Dimensional Portfolio Allocations?
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100207732
dc.identifier.doihttp://dx.doi.org/10.18452/4454
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages46
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2013
local.edoc.container-issue14
local.edoc.container-year2013
local.edoc.container-erstkatid2195055-6

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