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Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange

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dc.contributor.authorBildirici, Melike
dc.contributor.authorErsin, Oezguer Oemer
dc.contributor.institutionauthorBİLDİRİCİ, Melike Elif
dc.date.accessioned2026-06-27T13:08:53Z
dc.date.issued2009
dc.description.abstractIn the study, we discussed the ARCH/GARCH family models and enhanced them with artificial neural networks to evaluate the volatility of daily returns for 23.10.1987-22.02.2008 period in Istanbul Stock Exchange. We proposed ANN-APGARCH model to increase the forecasting performance of APGARCH model. The ANN-extended versions of the obtained GARCH models improved forecast results. It is noteworthy that daily returns in the ISE show strong volatility clustering, asymmetry and nonlinearity characteristics. (C) 2008 Elsevier Ltd. All rights reserved.en
dc.description.urihttps://doi.org/10.1016/j.eswa.2008.09.051
dc.identifier.doi10.1016/j.eswa.2008.09.051
dc.identifier.eissn1873-6793
dc.identifier.endpage7362
dc.identifier.issn0957-4174
dc.identifier.issue4
dc.identifier.startpage7355
dc.identifier.urihttps://hdl.handle.net/20.500.14981/50383
dc.identifier.volume36
dc.identifier.wos000264528600003
dc.language.isoeng
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONS
dc.subjectVolatility
dc.subjectStock returns
dc.subjectARCH/GARCH
dc.subjectEGARCH
dc.subjectTGARCH
dc.subjectPGARCH
dc.subjectAPGARCH
dc.subjectArtificial neural networks
dc.subjectAUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY
dc.subjectS-AND-P
dc.subjectPERCEPTRON
dc.subjectVARIANCE
dc.subjectComputer Science
dc.subjectEngineering
dc.subjectOperations Research & Management Science
dc.titleImproving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange
dc.typeArticle
dspace.entity.typePublication
local.import.sourceWOS

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