Rodyti trumpą aprašą

dc.contributor.authorGarcía, Fernando
dc.contributor.authorGuijarro, Francisco
dc.contributor.authorOliver, Javier
dc.date.accessioned2024-06-20T09:33:11Z
dc.date.available2024-06-20T09:33:11Z
dc.date.issued2014
dc.identifier.isbn9786094576508en_US
dc.identifier.issn2029-4441en_US
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/154514
dc.description.abstractThe analysis of conditional volatility is a key factor to correctly assess the risk of several financial assets such as shares, bonds or index as well as derivatives (futures and options). The econometric models from the GARCH family are traditionally the most widely used to predict conditional volatility. As an alternative to the econometric models, neural networks can be employed to this end. This paper compares the econometric model ARMA-EGARCH with the neuronal network Backpropagation. Both methodologies have been applied on diverse international stock indices. The main conclusion to be stressed is that the neuronal network can significantly better predict conditional volatility than the econometric model.en_US
dc.format.extent7 p.en_US
dc.format.mediumTekstas / Texten_US
dc.language.isoenen_US
dc.relation.urihttps://etalpykla.vilniustech.lt/handle/123456789/154365en_US
dc.source.urihttp://old.konferencijos.vgtu.lt/bm.vgtu.lt/public_html/index.php/bm/bm_2014/paper/view/466en_US
dc.subjectConditional volatilityen_US
dc.subjectGARCHen_US
dc.subjectBackpropagation neuronal networken_US
dc.subjectstock indexen_US
dc.subjectpredictionen_US
dc.titleModelling conditional volatility in stock indices: a comparison of the arma-egarch model versus neuronal network backpropagationen_US
dc.typeKonferencijos publikacija / Conference paperen_US
dcterms.accessRightsLaisvai prieinamas / Openly availableen_US
dcterms.accrualMethodRankinis pateikimas / Manual submissionen_US
dcterms.alternativeFinance engineeringen_US
dcterms.issued2014-05-16
dcterms.references31en_US
dc.description.versionTaip / Yesen_US
dc.contributor.institutionUniversidad Politécnica de Valenciaen_US
dcterms.sourcetitle8th International Scientific Conference “Business and Management 2014”en_US
dc.identifier.eisbn9786094576492en_US
dc.identifier.eissn2029-929Xen_US
dc.publisher.nameVilnius Gediminas Technical Universityen_US
dc.publisher.nameVilniaus Gedimino technikos universitetasen_US
dc.publisher.countryLithuaniaen_US
dc.publisher.countryLietuvaen_US
dc.publisher.cityVilniusen_US
dc.identifier.doihttp://dx.doi.org/10.3846/bm.2014.026en_US


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