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dc.contributor.authorAbanto-Valle, Carlos A.
dc.contributor.authorRodríguez, Gabriel
dc.contributor.authorGarrafa-Aragón, Hernán
dc.contributor.authorCastro Cepero, Luis M.
dc.date.accessioned2021-11-24T20:32:52Z
dc.date.available2021-11-24T20:32:52Z
dc.date.issued2021-10
dc.identifier.urihttps://repositorio.pucp.edu.pe/index/handle/123456789/182549
dc.description.abstractThe stochastic volatility in mean (SVM) model proposed by Koopman and Uspensky (2002) is revisited. This paper has two goals. The first is to offer a methodology that requires less computational time in simulations and estimates compared with others proposed in the literature as in Abanto-Valle et al. (2021) and others. To achieve the first goal, we propose to approximate the likelihood function of the SVM model applying Hidden Markov Models (HMM) machinery to make possible Bayesian inference in real-time. We sample from then posterior distribution of parameters with a multivariate Normal distribution with mean and variance given by the posterior mode and the inverse of the Hessian matrix evaluated at this posterior mode using importanc sampling (IS). The frequentist properties of estimators is anlyzed conducting a simulation study. The second goal is to provide empirical evidence estimating the SVM model using daily data for five Latin American stock markets. The results indicate that volatility negatively impacts returns, suggesting that the volatility feedback effect is stronger than the effect related to the expected volatility. This result is exact and opposite to the finding of Koopman and Uspensky (2002). We compare our methodology with the Hamiltonian Monte Carlo (HMC) and Riemannian HMC methods based on Abanto-Valle et al. (2021).es_ES
dc.language.isoenges_ES
dc.publisherPontificia Universidad Católica del Perú. Departamento de Economíaes_ES
dc.relation.ispartofurn:issn:2079-8474
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/pe/*
dc.subjectMercado Bursátiles de América Latinaes_ES
dc.subjectVolatilidad Estocástica en Mediaes_ES
dc.subjectEfecto Feed-Backes_ES
dc.subjectHamiltonian Monte Carloes_ES
dc.subjectHidden Markov Modelses_ES
dc.subjectRiemannian Manifold Hamiltonian Monte Carloes_ES
dc.subjectModelos Espacio Estado No Linealeses_ES
dc.titleApproximate bayesian estimation of stochastic volatility in mean models using hidden Markov models: empirical evidence from stock Latin American marketses_ES
dc.typeinfo:eu-repo/semantics/workingPaper
dc.type.otherDocumento de trabajo
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#5.02.00
dc.publisher.countryPE
dc.identifier.doihttp://doi.org/10.18800/2079-8474.0502


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