Asymmetric power and fractionally integrated support vector and neural network GARCH models with an application to forecasting financial returns in ise100 stock index

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dc.contributor.authorBildirici M.
dc.contributor.authorErsin Ö.Ö.
dc.date.accessioned2024-03-13T10:01:26Z
dc.date.available2024-03-13T10:01:26Z
dc.date.issued2014
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractThe study aims to augment commonly applied volatility models with support vector machines and neural networks. Further, fractional integration and asymmetric powers will be introduced. The proposed modeling strategy benefits from neural network based GARCH models and SVR-GARCH models. Following these approaches, the study proposed fractional integration and asymmetric power GARCH structures to obtain SVR-FIAPGARCH and NN-FIAPGARCH models to be evaluated in terms of learning algorithms. Models are evaluated for in-sample and out-of-sample forecasting of daily returns in Istanbul ISE100 stock index. Results suggest several findings: i. fractional integration and asymmetric power structures could be modeled with learning algorithms. ii. volatility clustering, asymmetry and nonlinearity characteristics are modeled more effectively with SVR-GARCH and MLP-GARCH models compared to the GARCH models. iii. SVR-GARCH models provided the lowest error criteria levels in out-of-sample and are closely followed by the MLP-GARCH models. © 2015, Academy of Economic Studies, All right reserved.en_US
dc.identifier.endpage22en_US
dc.identifier.issn0424-267X
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-84945175388en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3206
dc.identifier.volume48en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAcademy of Economic Studiesen_US
dc.relation.ispartofEconomic Computation and Economic Cybernetics Studies and Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectARCHen_US
dc.subjectFractional Integrationen_US
dc.subjectMLPen_US
dc.subjectStock Returnsen_US
dc.subjectVolatilityen_US
dc.titleAsymmetric power and fractionally integrated support vector and neural network GARCH models with an application to forecasting financial returns in ise100 stock indexen_US
dc.typeArticleen_US

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