Sampling distributions of optimal portfolio weights and characteristics in low and large dimensions

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dc.contributor.authorBodnar, Taras
dc.contributor.authorDette, Holger
dc.contributor.authorParolya, Nestor
dc.contributor.authorThorsén, Erik
dc.date.accessioned2019-09-06T13:23:21Z
dc.date.available2019-09-06T13:23:21Z
dc.date.issued2019
dc.description.abstractOptimal portfolio selection problems are determined by the (unknown) parameters of the data generating process. If an investor want to realise the position suggested by the optimal portfolios he/she needs to estimate the unknown parameters and to account the parameter uncertainty into the decision process. Most often, the parameters of interest are the population mean vector and the population covariance matrix of the asset re turn distribution. In this paper we characterise the exact sampling distribution of the estimated optimal portfolio weights and their characteristics by deriving their sampling distribution which is present in terms of a stochastic representation. This approach pos sesses several advantages, like (i) it determines the sampling distribution of the estimated optimal portfolio weights by expressions which could be used to draw samples from this distribution efficiently; (ii) the application of the derived stochastic representation pro vides an easy way to obtain the asymptotic approximation of the sampling distribution. The later property is used to show that the high-dimensional asymptotic distribution of optimal portfolio weights is a multivariate normal and to determine its parameters. Moreover, a consistent estimator of optimal portfolio weights and their characteristics is derived under the high-dimensional settings. Via an extensive simulation study, we investigate the finite-sample performance of the derived asymptotic approximation and study its robustness to the violation of the model assumptions used in the derivation of the theoretical results.en
dc.identifier.urihttp://hdl.handle.net/2003/38204
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-20183
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;17/2019
dc.subjectsampling distributionen
dc.subjecthigh-dimensional asymptoticsen
dc.subjectstochastic rep resentationen
dc.subjectparameter uncertaintyen
dc.subjectoptimal portfolioen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleSampling distributions of optimal portfolio weights and characteristics in low and large dimensionsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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