A Bayesian Quantile Time Series Model for Asset Returns. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Griffin, Jim E.; Mitrodima, Gelly
• Is Part Of: Journal of business & economic statistics. Volume 40:Issue 1(2022)
• Page Start: 16
• Page End: 27
• Publication Date: 2022-01-02
• Subjects: Bayesian nonparametrics -- Predictive density -- Stationarity -- Transformation models; Economics -- Statistical methods -- Periodicals; Commercial statistics -- Periodicals; Économie politique -- Méthodes statistiques -- Périodiques; Statistique commerciale -- Périodiques; 330.015195
• DOI: 10.1080/07350015.2020.1766470
• Languages: English
• ISSNs: 0735-0015

Abstract

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.