Using a dynamic artificial neural network for forecasting the volatility of a financial time series.

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dc.audienceComunidad Universidad de Medellínspa
dc.contributor.authorVelásquez, Juan D.
dc.contributor.authorGutiérrez, Sarah
dc.contributor.authorFranco, Carlos J.
dc.date.accessioned2014-10-22T23:26:11Z
dc.date.available2014-10-22T23:26:11Z
dc.date.issued2013-06-30
dc.description.abstractThe ability to obtain accurate volatility forecasts is an important issue for the financial analyst. In this paper, we use the DAN2 model, a multilayer perceptronand an ARCH model to predict the monthly conditional variance of stock prices.The results show that DAN2 model is more accurate for predicting in-sample andout-of-sample variance that the other considered models for the used data set. Thus, the value of this neural network as a predictive tool is demonstrated.spa
dc.format.mediumElectrónicospa
dc.format.mimetypeapplication/pdf
dc.identifier.eissn2248-4094
dc.identifier.instnameinstname:Universidad de Medellínspa
dc.identifier.issn1692-3324
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.urihttp://hdl.handle.net/11407/962
dc.language.isospa
dc.publisherUniversidad de Medellínspa
dc.publisher.facultyFacultad de Ingenieríasspa
dc.publisher.placeMedellínspa
dc.relation.ispartofjournalRevista Ingenierías Universidad de Medellínspa
dc.relation.urihttp://revistas.udem.edu.co/index.php/ingenierias/article/view/637
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.creativecommonsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.sourceRevista Ingenierías Universidad de Medellín; Vol. 12, núm. 22 (2013)spa
dc.source2248-4094spa
dc.source1692-3324spa
dc.subjectVolatility forecastspa
dc.subjectpredictionspa
dc.subjectnonlinear modelsspa
dc.subjectheteroskedasticityspa
dc.subjectvolatilidad (finanzas)spa
dc.subjectmodelos no linealesspa
dc.subjectheterocedasticidadspa
dc.titleUsing a dynamic artificial neural network for forecasting the volatility of a financial time series.spa
dc.typeArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.driverinfo:eu-repo/semantics/article
dc.type.localArtículo científicospa
dc.type.versioninfo:eu-repo/semantics/publishedVersion

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Revista Ingenierías Universidad de Medellín Vol. 12, núm. 22 (2013)