Particle Learning for Bayesian Semi-Parametric Stochastic Volatility Model
Audrone Virbickaite,
Hedibert F. Lopes (hedibertfl@insper.edu.br),
Maria Concepción Ausín (concepcion.ausin@uc3m.es) and
Pedro Galeano (pedro.galeano@uc3m.es)
Additional contact information
Hedibert F. Lopes: Insper Institute of Education and Research, Postal: Quatá Street, 300 - Vila Olímpia, São Paulo - SP, 04546-042, http://hedibert.org/
Maria Concepción Ausín: Universidad Carlos III de Madrid, Postal: Calle Madrid, 126, 28903 Getafe, Madrid
Pedro Galeano: Universidad Carlos III de Madrid, Postal: Calle Madrid, 126, 28903 Getafe, Madrid
No 88, DEA Working Papers from Universitat de les Illes Balears, Departament d'Economía Aplicada
Abstract:
This paper 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 MCMC, since they allow for online type inference. The posterior distributions are updated as new data is observed, which is prohibitively 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.
Keywords: Bayes factor; Dirichlet Process Mixture; MCMC; Sequential Monte Carlo. (search for similar items in EconPapers)
JEL-codes: C11 C14 C58 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (1)
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http://www.uib.es/depart/deaweb/deawp/pdf/w88.pdf (application/pdf)
Related works:
Journal Article: Particle learning for Bayesian semi-parametric stochastic volatility model (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:ubi:deawps:88
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