A Bayesian quantile time series model for asset returns

Jim E. Griffin and Gelly Mitrodima

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: Bayesian nonparametrics; Predictive density; Stationarity; Transformation models (search for similar items in EconPapers)
JEL-codes: C1 J1 (search for similar items in EconPapers)
Date: 2020-06-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (4)

Published in Journal of Business and Economic Statistics, 10, June, 2020. ISSN: 0735-0015

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:105610

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