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29 06 im noticia Ciclo de Palestras PPG EstatísticaTítulo: Approximate Bayesian Estimation of Stochastic Volatility in Mean Models using Hidden Markov Models: Empirical Evidence from Stock Latin American Markets

Palestrante: Carlos Antonio Abanto-Valle (DME-UFRJ)
Data: 30/06/2021
Horário: 15:30h
Local: Transmissão online

Resumo: The stochastic volatility in mean (SVM) model proposed by Koopman and Uspensky (2002) is revisited. We offer 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 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 (IS). We conduct a simulation study to verify the frequentist properties of estimators. An empirical analysis of five Latin American indexes to see the impact of the volatility in the mean of the returns is performed. 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).

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