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dc.contributor.author | García García, Fernando | es_ES |
dc.contributor.author | Guijarro Martínez, Francisco | es_ES |
dc.contributor.author | Moya Clemente, Ismael | es_ES |
dc.contributor.author | Oliver Muncharaz, Javier | es_ES |
dc.date.accessioned | 2016-01-18T16:04:31Z | |
dc.date.available | 2016-01-18T16:04:31Z | |
dc.date.issued | 2012-12 | |
dc.identifier.issn | 2174-6036 | |
dc.identifier.uri | http://hdl.handle.net/10251/60000 | |
dc.description | Creative Commons: Reconocimiento 3.0 España (CC BY 3.0 ES) | es_ES |
dc.description.abstract | Econometric models have usually estimated both returns and conditional volatility in financial assets. This paper is intended in the comparison of this traditional approach with the more recent Backpropagation neural network. When applied to the Spanish Ibex-35 stock market index, we find that the neural network achieved significantly better performance in predicting conditional volatility, but similar results when predicting financial returns. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Interlude | es_ES |
dc.relation.ispartof | International Journal of Complex Systems in Science | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Conditional volatility | es_ES |
dc.subject | Backpropagation neural network | es_ES |
dc.subject | GARCH-M | es_ES |
dc.subject.classification | ECONOMIA FINANCIERA Y CONTABILIDAD | es_ES |
dc.title | Estimating returns and condicional volatility: a comparison between the ARMA-GARCH-M Models and the Backpropagation Neural Network | es_ES |
dc.type | Artículo | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Economía y Ciencias Sociales - Departament d'Economia i Ciències Socials | es_ES |
dc.description.bibliographicCitation | García García, F.; Guijarro Martínez, F.; Moya Clemente, I.; Oliver Muncharaz, J. (2012). Estimating returns and condicional volatility: a comparison between the ARMA-GARCH-M Models and the Backpropagation Neural Network. International Journal of Complex Systems in Science. 2(1):21-26. http://hdl.handle.net/10251/60000 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://www.ij-css.org/vol_02-01.html | es_ES |
dc.description.upvformatpinicio | 21 | es_ES |
dc.description.upvformatpfin | 26 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 2 | es_ES |
dc.description.issue | 1 | es_ES |
dc.relation.senia | 232040 | es_ES |