000131121 001__ 131121
000131121 005__ 20240205173707.0
000131121 0247_ $$2doi$$a10.1080/13504851.2012.669454
000131121 0248_ $$2sideral$$a77186
000131121 037__ $$aART-2012-77186
000131121 041__ $$aeng
000131121 100__ $$0(orcid)0000-0002-2525-9049$$aAlda, Mercedes$$uUniversidad de Zaragoza
000131121 245__ $$aLinear and nonlinear financial time series: Evidence in a sample of pension funds in Spain and the United Kingdom
000131121 260__ $$c2012
000131121 5203_ $$aIn this article, we examine whether traditional linear models are suitable to assess financial samples, because financial data usually present nonnormality or nonlinear patterns, therefore linear models do not always adequately capture them. For this reason, as returns series usually follow autoregressive patterns, nonlinear models such as Self-Exciting Threshold Autoregressive (SETAR), Logistic STAR (LSTAR), Additive Autoregressive (AAR) or Neural Network (NNET) could provide a good fit. We study whether two samples of pension funds' returns in Spain and the United Kingdom present these features, and we find that the most appropriate model for the Spanish sample is an autoregressive model, but in the United Kingdom sample, we fit a neural network.
000131121 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000131121 590__ $$a0.295$$b2012
000131121 591__ $$aECONOMICS$$b273 / 333 = 0.82$$c2012$$dQ4$$eT3
000131121 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000131121 700__ $$0(orcid)0000-0002-3816-9747$$aFerruz, Luis$$uUniversidad de Zaragoza
000131121 7102_ $$14002$$2230$$aUniversidad de Zaragoza$$bDpto. Contabilidad y Finanzas$$cÁrea Economía Finan. y Contab.
000131121 773__ $$g19, 18 (2012), 1933-1937$$pAppl. econ. lett.$$tAPPLIED ECONOMICS LETTERS$$x1350-4851
000131121 8564_ $$s93945$$uhttps://zaguan.unizar.es/record/131121/files/texto_completo.pdf$$yPostprint
000131121 8564_ $$s1910770$$uhttps://zaguan.unizar.es/record/131121/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000131121 909CO $$ooai:zaguan.unizar.es:131121$$particulos$$pdriver
000131121 951__ $$a2024-02-05-15:06:35
000131121 980__ $$aARTICLE