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2000-06-13Buch DOI: 10.18452/8238
Mean-variance versus expected utility in dynamic investment analysis
dc.contributor.authorZiemba, William T.
dc.contributor.authorZhao, Yonggan
dc.contributor.editorHigle, Julie L.
dc.contributor.editorRömisch, Werner
dc.contributor.editorSen, Surrajeet
dc.date.accessioned2017-06-16T19:39:05Z
dc.date.available2017-06-16T19:39:05Z
dc.date.created2006-02-09
dc.date.issued2000-06-13
dc.date.submitted2000-06-13
dc.identifier.urihttp://edoc.hu-berlin.de/18452/8890
dc.description.abstractThis paper derives the mean-variance efficient frontier and optimal portfolio policies for a dynamic investment model. In the absence of arbitrage opportunities, the optimal expected portfolio value can be identified through the state price density in a frictionless market using martingale analysis. The efficient frontier for the dynamic model is linear in the space of the standard deviation and the expected value of the terminal portfolio in the presence of a riskless asset as in the static mean-variance case. A replication procedure is developed to obtain the optimal portfolio policies using a partial differential equation. A closed form solution is derived if asset prices jointly follow a multidimensional geometric Brownian motion. A comparison is made between the optimal policies of the expected utility approach and a mean-variance analysis in the continuous time setting. For investors interested in the mean-variance criterion, we discuss and derive the optimal choice of target wealth that maximizes the probability that the mean-variance analysis outperforms the expected utility approach.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc510 Mathematik
dc.titleMean-variance versus expected utility in dynamic investment analysis
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10057675
dc.identifier.doihttp://dx.doi.org/10.18452/8238
local.edoc.container-titleStochastic Programming E-Print Series
local.edoc.pages29
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2000
local.edoc.container-issue14
local.edoc.container-erstkatid2936317-2

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