Particle learning for Bayesian semi-parametric stochasticvolatility model

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Resumo

This article designs a Sequential Monte Carlo (SMC) algorithm for estimation of Bayesian semi-parametric Stochastic Volatility model for financial data. In particular, it makes use of one of the most recent particle filters called Particle Learning (PL). SMC methods are especially well suited for state-space models and can be seen as a cost-efficient alternative to Markov Chain Monte Carlo (MCMC), since they allow for online type inference. The posterior distributions are updated as new data is observed, which is exceedingly costly using MCMC. Also, PL allows for consistent online model comparison using sequential predictive log Bayes factors. A simulated data is used in order to compare the posterior outputs for the PL and MCMC schemes, which are shown to be almost identical. Finally, a short real data application is included.

Palavras-chave

Bayes factor; Dirichlet Process Mixture; MCMC; Sequential Monte Carlo
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Titulo de periódico

Econometric Reviews
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Membros da banca

Área do Conhecimento CNPQ

CIENCIAS EXATAS E DA TERRA::MATEMATICA

CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA

CIENCIAS SOCIAIS APLICADAS::ECONOMIA

CIENCIAS SOCIAIS APLICADAS::ECONOMIA::METODOS QUANTITATIVOS EM ECONOMIA

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