@article{discovery10097063,
       publisher = {American Statistical Association},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
           title = {A Bayesian Quantile Time Series Model for Asset Returns},
         journal = {Journal of Business \& Economic Statistics},
            year = {2020},
            issn = {0735-0015},
        keywords = {Bayesian nonparametrics, Predictive density, Stationarity, Transformation models},
        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.},
          author = {Griffin, JE and Mitrodima, G},
             url = {https://doi.org/10.1080/07350015.2020.1766470}
}