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Bayesian Semi-Parametric Markov Switching Stochastic Volatility Model

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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.

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  • Audrone Virbickaite & Hedibert F. Lopes, 2018. "Bayesian Semi-Parametric Markov Switching Stochastic Volatility Model," DEA Working Papers 89, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    • Handle: RePEc:ubi:deawps:89
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    More about this item

    Keywords

    Bayes Factor; Dirichlet Process Mixture; Particle Learning; Sequential Monte Carlo.;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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