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A Bayesian Quantile Time Series Model for Asset Returns

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  • Jim E. Griffin
  • Gelly Mitrodima

Abstract

We consider jointly modeling a finite collection of quantiles over time. Formal Bayesian inference on quantiles is challenging since we need access to both the quantile function and the likelihood. We propose a flexible Bayesian time-varying transformation model, which allows the likelihood and the quantile function to be directly calculated. We derive conditions for stationarity, discuss suitable priors, and describe a Markov chain Monte Carlo algorithm for inference. We illustrate the usefulness of the model for estimation and forecasting on stock, index, and commodity returns.

Suggested Citation

  • Jim E. Griffin & Gelly Mitrodima, 2022. "A Bayesian Quantile Time Series Model for Asset Returns," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 16-27, January.
  • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:1:p:16-27
    DOI: 10.1080/07350015.2020.1766470
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    Cited by:

    1. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).

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