eprintid: 10097063 rev_number: 21 eprint_status: archive userid: 608 dir: disk0/10/09/70/63 datestamp: 2020-05-12 10:33:44 lastmod: 2022-01-03 00:06:53 status_changed: 2020-05-12 10:33:44 type: article metadata_visibility: show creators_name: Griffin, JE creators_name: Mitrodima, G title: A Bayesian Quantile Time Series Model for Asset Returns ispublished: inpress divisions: UCL divisions: B04 divisions: C06 divisions: F61 keywords: Bayesian nonparametrics, Predictive density, Stationarity, Transformation models note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. 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: 2020 date_type: published publisher: American Statistical Association official_url: https://doi.org/10.1080/07350015.2020.1766470 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1781330 doi: 10.1080/07350015.2020.1766470 lyricists_name: Griffin, James lyricists_id: JEGRI73 actors_name: Griffin, James actors_id: JEGRI73 actors_role: owner full_text_status: public publication: Journal of Business & Economic Statistics issn: 0735-0015 citation: Griffin, JE; Mitrodima, G; (2020) A Bayesian Quantile Time Series Model for Asset Returns. Journal of Business & Economic Statistics 10.1080/07350015.2020.1766470 <https://doi.org/10.1080/07350015.2020.1766470>. (In press). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10097063/1/BayesianJQTSmodels_revision_21_4_20.pdf