Particle learning for Bayesian semi-parametric stochastic volatility model |
| |
Authors: | Audronė Virbickaitė Hedibert F. Lopes M. Concepción Ausín Pedro Galeano |
| |
Affiliation: | 1. Department of Applied Economics, Universitat de les Illes Balears (UIB), Palma de Mallorca, Spain;2. Insper Institute of Education and Research, Sao Paulo, Brazil;3. Department of Statistics and Institute UC3M-BS of Financial Big Data, Universidad Carlos III de Madrid, Getafe, Madrid, Spain |
| |
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. |
| |
Keywords: | Bayes factor Dirichlet Process Mixture MCMC Sequential Monte Carlo |
|
|