Bayesian Semi-Parametric Markov Switching Stochastic Volatility Model

Audrone Virbickaite () and Hedibert F. Lopes ()
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Audrone Virbickaite: Universitat de les Illes Balears, Postal: Edifici Jovellanos, Crta Valldemossa, km 7,5 07122 Palma de Mallorca (Spain), https://sites.google.com/view/audravirbickaitephd
Hedibert F. Lopes: Insper Institute of Education and Research, Postal: Quatá Street, 300 - Vila Olímpia, São Paulo - SP, 04546-042, http://hedibert.org/

No 89, DEA Working Papers from Universitat de les Illes Balears, Departament d'Economía Aplicada

Abstract: This paper proposes a novel Bayesian semi-parametric Stochastic Volatility model with Markov switching regimes for modeling the dynamics of the financial returns. The distribution of the error term of the returns is modeled as an infinite mixture of Normals, meanwhile the intercept of the volatility equation is allowed to switch between two regimes. The proposed model is estimated using a novel sequential Monte Carlo method called Particle Learning that is especially well suited for state-space models. The model is tested on simulated data and, using real financial times series, compared to a model without the Markov switching regimes. The results show that including a Markov switching specification provides higher predictive power for the entire distribution, as well as in the tails of the distribution. Finally, the estimate of the persistence parameter decreases significantly, a finding consistent with previous empirical studies.

Keywords: Bayes Factor; Dirichlet Process Mixture; Particle Learning; Sequential Monte Carlo. (search for similar items in EconPapers)
JEL-codes: C11 C14 C58 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-ets and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:ubi:deawps:89

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