Particle learning for Bayesian non-parametric Markov Switching Stochastic Volatility model

Hedibert F. Lopes
Authors registered in the RePEc Author Service: Pedro Galeano

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

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.

Keywords: Sequential; Monte; Carlo; Dirichlet; Process; Mixture; Markov; Switching; MCMC; Particle; Learning; Stochastic; Volatility (search for similar items in EconPapers)
Date: 2014-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:ws142819

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