Approximate Bayesian Estimation of Stochastic Volatility in Mean Models Using Hidden Markov Models: Empirical Evidence from Emerging and Developed Markets

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dc.contributor.authorAbanto-Valle, Carlos A.
dc.contributor.authorRodriguez, Gabriel
dc.contributor.authorCastro Cepero, Luis M.
dc.contributor.authorGarrafa-Aragon, Hernan B.
dc.date.accessioned2025-01-20T17:11:29Z
dc.date.available2025-01-20T17:11:29Z
dc.date.issued2024
dc.description.abstractThe stochastic volatility in mean (SVM) model proposed by Koopman and Uspensky (J Appl Econ 17:667-689, 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. (Q Rev Econ Financ 80:272-286, 2021) and others. To achieve the first goal, we propose to approximate the likelihood function of the model applying Hidden Markov Models machinery to make possible Bayesian inference in real-time. We sample from the 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 importance sampling. Further, the frequentist properties of estimators are analyzed 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, USA, England, Japan and China. 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 similar to the findings of Koopman and Uspensky (J Appl Econ 17:667-689, 2002), where the respective coefficient is negative but non statistically significant. However, in our case, all countries (except Peru and China) presents negative and statistically significant effects. Our results are similar to those found using Hamiltonian Monte Carlo (HMC) and Riemannian HMC methods based on Abanto-Valle et al. (Q Rev Econ Financ 80:272-286, 2021).
dc.description.funderWe would like to thank the Editor and two anonymous Referees for their useful comments, which improved the quality of this paper.
dc.fuente.origenWOS
dc.identifier.doi10.1007/s10614-023-10490-4
dc.identifier.eissn1572-9974
dc.identifier.issn0927-7099
dc.identifier.urihttps://doi.org/10.1007/s10614-023-10490-4
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/91186
dc.identifier.wosidWOS:001092626200001
dc.issue.numero3
dc.language.isoen
dc.pagina.final1801
dc.pagina.inicio1775
dc.revistaComputational economics
dc.rightsacceso restringido
dc.subjectStock Latin American markets
dc.subjectStochastic volatility in mean
dc.subjectFeed-back effect
dc.subjectHamiltonian Monte Carlo
dc.subjectHidden Markov Models
dc.subjectRiemannian Manifold Hamiltonian Monte Carlo
dc.subjectNon linear state space models
dc.subjectC11
dc.subjectC15
dc.subjectC22
dc.subjectC51
dc.subjectC52
dc.subjectC58
dc.subjectG12
dc.subject.ods08 Decent Work and Economic Growth
dc.subject.odspa08 Trabajo decente y crecimiento económico
dc.titleApproximate Bayesian Estimation of Stochastic Volatility in Mean Models Using Hidden Markov Models: Empirical Evidence from Emerging and Developed Markets
dc.typeartículo
dc.volumen64
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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