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dc.contributor.authorSánchez-Granero, M.A
dc.contributor.authorFernández-Martínez, M.
dc.contributor.authorTrinidad Segovia, J.E
dc.date.accessioned2017-06-16T09:42:33Z
dc.date.available2017-06-16T09:42:33Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10835/4866
dc.description.abstractIn this paper, three new algorithms are introduced in order to explore long memory in financial time series. They are based on a new concept of fractal dimension of a curve. A mathematical support is provided for each algorithm and its accuracy is tested for different length time series by Monte Carlo simulations. In particular, in the case of short length series, the introduced algorithms perform much better than the classical methods. Finally, an empirical application for some stock market indexes as well as some individual stocks is presented.es_ES
dc.language.isoenes_ES
dc.publisherTHE EUROPEAN PHYSICAL JOURNALes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleIntroducing fractal dimension algorithms to calculate the Hurst exponent of financial time serieses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.epj.org/articles/epjb/abs/2012/03/b110803/b110803.htmles_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1140/epjb/e2012-20803-2


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional