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dc.contributor.authorNabati, Parisaen_US
dc.date.accessioned2022-07-21T13:28:46Z
dc.date.available2022-07-21T13:28:46Z
dc.date.issued2022
dc.identifier.citationNabati, P. (2022). The mean reverting Ornstein-Uhlenbeck processes with nonlinear autoregressive drift term innovations. TWMS Journal Of Applied And Engineering Mathematics, 12(3), 931-939.en_US
dc.identifier.urihttp://belgelik.isikun.edu.tr/xmlui/handle/iubelgelik/4689
dc.identifier.urihttp://jaem.isikun.edu.tr/web/index.php/archive/116-vol12no3/878
dc.description.abstractThe main purpose of this paper is to present a new approach for energy markets governed by a two-factor Ornstein-Uhlenbeck process with a stochastic nonlinear autoregressive drift term innovation and an unknown diffusion coefficient. This model has interesting characteristics: since the drift is stochastic, it allows for price to fluctuate around a level that is not fixed. A semiparametric method is proposed to estimate the nonlinear regression function based on the conditional least square method for parametric estimation and the nonparametric kernel approach for the AR adjustment estimation. For estimating the diffusion coefficient of the Ornstein-Uhlenbeck process from discretely observed data a semiparametric approach based on the least-squares estimator is carried out. Finally, numerical simulations are performed using Matlab programming to show efficiency and the accuracy of the present work.en_US
dc.language.isoenen_US
dc.publisherIşık University Pressen_US
dc.relation.ispartofTWMS Journal Of Applied And Engineering Mathematicsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectOrnstein-Uhlenbeck processesen_US
dc.subjectNonlinear autoregressive modelen_US
dc.subjectSemiparametric estimationen_US
dc.subjectSmooth kernel approachen_US
dc.subjectConditional nonlinear least-squares methoden_US
dc.titleThe mean reverting Ornstein-Uhlenbeck processes with nonlinear autoregressive drift term innovationsen_US
dc.typeArticleen_US
dc.description.versionPublisher's Versionen_US
dc.identifier.volume12
dc.identifier.issue3
dc.identifier.startpage931
dc.identifier.endpage939
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıen_US


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