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dc.contributor.authorNeuenkirch, Andreas-
dc.contributor.authorZähle, Henryk-
dc.description.abstractIn [14, 8] Kurtz and Protter resp. Jacod and Protter specify the asymptotic error distribution of the Euler method for stochastic differential equations (SDEs) with smooth coefficients growing at most linearly. The required differentiability and linear growth of the coefficients rule out some popular SDEs as for instance the Cox-Ingersoll-Ross (CIR) model, the Heston model, or the stochastic Brusselator. In this article, we partially extend one of the fundamental results in [8], so that also the mentioned examples are covered. Moreover, we compare by means of simulations the asymptotic error distributions of the CIR model and the geometric Brownian motion with mean reversion.en
dc.relation.ispartofseriesPreprints der Fakultät für Mathematik ; 2009-10de
dc.subjectstochastic differential equationen
dc.subjectEuler schemeen
dc.subjecterror processen
dc.subjectweak convergenceen
dc.titleAsymptotic error distribution of the Euler method for SDEs with non-Lipschitz coefficientsen
dcterms.accessRightsopen access-
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