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