Bayesian semiparametric Markov switching stochastic volatility model.

Virbickaitė, Audronė; Lopes, Hedibert F.

This paper proposes a novel Bayesian semiparametric 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.

MONTE Carlo method; STOCHASTIC models; MARKOV processes; STATE-space methods; TIME series analysis

Applied Stochastic Models in Business & Industry, 2019, Vol 35, Issue 4, p978

1524-1904

Academic Journal

10.1002/asmb.2434