A Bayesian Quantile Time Series Model for Asset Returns

Jim E. Griffin and Gelly Mitrodima

Journal of Business & Economic Statistics, 2022, vol. 40, issue 1, 16-27

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.

Date: 2022
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DOI: 10.1080/07350015.2020.1766470

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