Publication: Particle learning for Bayesian non-parametric Markov Switching Stochastic Volatility model
Loading...
Identifiers
Publication date
2014-10
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Impact
Export
Abstract
This paper designs a Particle Learning (PL) algorithm for estimation of Bayesian nonparametric Stochastic Volatility (SV) models for financial data. The performance of this particle method is then compared with the standard Markov Chain Monte Carlo (MCMC) methods for non-parametric SV models. PL performs as well as MCMC, and at the same time allows for on-line type inference. The posterior distributions are updated as new data is observed, which is prohibitively costly using MCMC. Further, a new non-parametric SV model is proposed that incorporates Markov switching jumps.The proposed model is estimated by using PL and tested on simulated data. Finally, the performance of the two non-parametric SV models, with and without Markov switching, is compared by using real financial time series. The results show that including a Markov switching specification provides higher predictive power in the tails of the distribution.
Description
Keywords
Dirichlet Process Mixture, Markov Switching, MCMC, Particle Learning, Stochastic Volatility, Sequential Monte Carlo