Particle learning for Bayesian semi-parametric stochasticvolatility model
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
Vínculo institucional
Titulo de periódico
Econometric Reviews
DOI
Título de Livro
URL na Scopus
Idioma
Inglês
Notas
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
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
CIENCIAS SOCIAIS APLICADAS::ECONOMIA
CIENCIAS SOCIAIS APLICADAS::ECONOMIA::METODOS QUANTITATIVOS EM ECONOMIA