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A Bayesian quantile time series model for asset returns

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  • Griffin, Jim E.
  • Mitrodima, Gelly

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

  • Griffin, Jim E. & Mitrodima, Gelly, 2020. "A Bayesian quantile time series model for asset returns," LSE Research Online Documents on Economics 105610, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:105610
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    File URL: http://eprints.lse.ac.uk/105610/
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    References listed on IDEAS

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    8. Yuzhi Cai & Julian Stander & Neville Davies, 2012. "A new Bayesian approach to quantile autoregressive time series model estimation and forecasting," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(4), pages 684-698, July.
    9. Smith, Michael Stanley & Maneesoonthorn, Worapree, 2018. "Inversion copulas from nonlinear state space models with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 34(3), pages 389-407.
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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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    3. De Santis, Roberto A. & Van der Veken, Wouter, 2020. "Forecasting macroeconomic risk in real time: Great and Covid-19 Recessions," Working Paper Series 2436, European Central Bank.
    4. Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021. "The time-varying evolution of inflation risks," Working Paper Series 2600, European Central Bank.

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    More about this item

    Keywords

    Bayesian nonparametrics; Predictive density; Stationarity; Transformation models;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • J1 - Labor and Demographic Economics - - Demographic Economics

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