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dc.contributor.authorOrtobelli, Sergio
dc.contributor.authorLando, Tommaso
dc.contributor.authorPetronio, Filomena
dc.contributor.authorTichý, Tomáš
dc.date.accessioned2016-04-05T11:43:18Z
dc.date.available2016-04-05T11:43:18Z
dc.date.issued2016
dc.identifier.citationJournal of Computational and Applied Mathematics. 2016, vol. 300, p. 432-448.cs
dc.identifier.issn0377-0427
dc.identifier.issn1879-1778
dc.identifier.urihttp://hdl.handle.net/10084/111439
dc.description.abstractIn this paper, we deal with stochastic dominance rules under the assumption that the random variables are stable distributed. The stable Paretian distribution is generally used to model a wide range of phenomena. In particular, its use in several applicative areas is mainly justified by the generalized central limit theorem, which states that the sum of a number of i.i.d. random variables with heavy tailed distributions tends to a stable Paretian distribution. We show that the asymptotic behavior of the tails is fundamental for establishing a dominance in the stable Paretian case. Moreover, we introduce a new weak stochastic order of dispersion, aimed at evaluating whether a random variable is more “risky” than another under condition of maximum uncertainty, and a stochastic order of asymmetry, aimed at evaluating whether a random variable is more or less asymmetric than another. The theoretical results are confirmed by a financial application of the obtained dominance rules. The empirical analysis shows that the weak order of risk introduced in this paper is generally a good indicator for the second order stochastic dominance.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesJournal of Computational and Applied Mathematicscs
dc.relation.urihttp://dx.doi.org/10.1016/j.cam.2015.12.017cs
dc.rightsCopyright © 2016 Elsevier B.V. All rights reserved.cs
dc.subjectAsymmetrycs
dc.subjectHeavy tailscs
dc.subjectStable Paretian distributioncs
dc.subjectStochastic dominancecs
dc.titleAsymptotic stochastic dominance rules for sums of i.i.d. random variablescs
dc.typearticlecs
dc.identifier.doi10.1016/j.cam.2015.12.017
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume300cs
dc.description.lastpage448cs
dc.description.firstpage432cs
dc.identifier.wos000371551300031


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