RT Generic
T1 Particle learning for Bayesian non-parametric Markov Switching Stochastic Volatility model
A1 Virbickaite, Audrone
A1 Lopes, Hedibert F.
A1 Ausín Olivera, María Concepción
A1 Galeano San Miguel, Pedro
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB 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.
YR 2014
FD 2014-10
LK https://hdl.handle.net/10016/19576
UL https://hdl.handle.net/10016/19576
LA eng
NO Virbickaite, A. and Ausín, C.M. are grateful for the financial support from MEC grant ECO2011-25706. Galeano, P. acknowledges financial support from MEC grant ECO2012-38442
DS e-Archivo
RD 28 may. 2024