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• W54380332 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." @default.
• W54380332 created "2016-06-24" @default.
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• W54380332 date "2014-10-01" @default.
• W54380332 modified "2023-09-23" @default.
• W54380332 title "Particle learning for Bayesian non-parametric Markov Switching Stochastic Volatility model" @default.
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• W54380332 hasPublicationYear "2014" @default.
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