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Bayesian Inference for Markov-switching Skewed Autoregressive Models

Author

Listed:
  • Stéphane Lhuissier
Abstract
We examine Markov-switching autoregressive models where the commonly used Gaussian assumption for disturbances is replaced with a skew-normal distribution. This allows us to detect regime changes not only in the mean and the variance of a specified time series, but also in its skewness. A Bayesian framework is developed based on Markov chain Monte Carlo sampling. Our informative prior distributions lead to closed-form full conditional posterior distributions, whose sampling can be efficiently conducted within a Gibbs sampling scheme. The usefulness of the methodology is illustrated with a real-data example from U.S. stock markets.

Suggested Citation

  • Stéphane Lhuissier, 2019. "Bayesian Inference for Markov-switching Skewed Autoregressive Models," Working papers 726, Banque de France.
    • Handle: RePEc:bfr:banfra:726
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    References listed on IDEAS

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    More about this item

    Keywords

    Regime switching; Skewness; Gibbs-sampler; time series analysis; upside and downside risks.;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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