Particle learning for Bayesian semi-parametric stochastic volatility model

Audronė Virbickaitė, Hedibert F. Lopes, M. Concepción Ausín, Pedro Galeano

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1007-1023
Number of pages17
JournalEconometric Reviews
Volume38
Issue number9
DOIs
StatePublished - Oct 21 2019
Externally publishedYes

Keywords

  • Bayes factor
  • Dirichlet Process Mixture
  • MCMC
  • Sequential Monte Carlo

ASJC Scopus subject areas

  • Economics and Econometrics

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