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dc.contributor.advisorBayes Rodríguez, Cristian Luises_ES
dc.contributor.authorLengua Lafosse, Patriciaes_ES
dc.date.accessioned2015-07-17T15:34:04Zes_ES
dc.date.available2015-07-17T15:34:04Zes_ES
dc.date.created2015es_ES
dc.date.issued2015-07-17es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12404/6167
dc.description.abstractThis paper represents empirical studies of stochastic volatility (SV) models for daily stocks returns data of a set of Latin American countries (Argentina, Brazil, Chile, Mexico and Peru) for the sample period 1996:01-2013:12. We estimate SV models incorporating both leverage effects and skewed heavy-tailed disturbances taking into account the GH Skew Student’s t-distribution using the Bayesian estimation method proposed by Nakajima and Omori (2012). A model comparison between the competing SV models with symmetric Student´s t-disturbances is provided using the log marginal likelihoods in the empirical study. A prior sensitivity analysis is also provided. The results suggest that there are leverage effects in all indices considered but there is not enough evidence for Peru, and skewed heavy-tailed disturbances is confirmed only for Argentina, symmetric heavy-tailed disturbances for Mexico, Brazil and Chile, and symmetric Normal disturbances for Peru. Furthermore, we find that the GH Skew Student s t-disturbance distribution in the SV model is successful in describing the distribution of the daily stock return data for Peru, Argentina and Brazil over the traditional symmetric Student´s t-disturbance distribution.es_ES
dc.description.uriTesises_ES
dc.language.isoenges_ES
dc.publisherPontificia Universidad Católica del Perúes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Perú*
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/pe/*
dc.subjectEstadística bayesianaes_ES
dc.subjectAnálisis estocásticoes_ES
dc.subjectBolsa de Valoreses_ES
dc.titleAn empirical application of stochastic volatility models to Latin-American stock returns using GH skew student's t-distributiones_ES
dc.typeinfo:eu-repo/semantics/masterThesises_ES
thesis.degree.nameMagíster en Estadísticaes_ES
thesis.degree.levelMaestríaes_ES
thesis.degree.grantorPontificia Universidad Católica del Perú. Escuela de Posgradoes_ES
thesis.degree.disciplineEstadísticaes_ES
dc.type.otherTesis de maestría
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.01.03es_ES
dc.publisher.countryPEes_ES
renati.advisor.dni40372640
renati.advisor.orcidhttps://orcid.org/0000-0003-0474-7921es_ES
renati.discipline542037es_ES
renati.levelhttps://purl.org/pe-repo/renati/level#maestroes_ES
renati.typehttp://purl.org/pe-repo/renati/type#tesises_ES


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Atribución-NoComercial-SinDerivadas 2.5 Perú
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 2.5 Perú