Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

79 Pages Posted: 17 Jan 2020 Last revised: 22 Sep 2020

See all articles by Andrea Carriero

Andrea Carriero

Queen Mary, University of London

Todd E. Clark

Federal Reserve Bank of Cleveland

Massimiliano Giuseppe Marcellino

Bocconi University - Department of Economics; Centre for Economic Policy Research (CEPR)

Date Written: September 21, 2020

Abstract

A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and it has relied on quantile regression methods to estimate tail risks. Although much of this work discusses asymmetries in conditional predictive distributions, the analysis often focuses on evidence of downside risk varying more than upside risk. We note that this pattern in risk estimates over time could obtain with conditional distributions that are symmetric but subject to simultaneous shifts in conditional means (down) and variances (up). Building on that insight, we examine the ability of Bayesian VARs with stochastic volatility to capture tail risks in macroeconomic forecast distributions and outcomes. We consider both a conventional stochastic volatility specification and a specification with a common factor in volatility that enters the VAR’s conditional mean. Even though the one-step-ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, the model estimates yield more time variation in downside risk as compared to upside risk. Results from the model that includes a volatility factor in the conditional mean and thereby allows for asymmetries in conditional distributions are very similar. Our paper also extends the recent literature by formally evaluating the accuracy of tail risk forecasts and assessing the performance of Bayesian quantile regression, as well as our Bayesian VARs, in this context. Overall, the BVAR models perform comparably to quantile regression for estimating and forecasting tail risks, complementing BVARs’ established performance for forecasting and structural analysis.

Keywords: forecasting, downside risk, asymmetries

JEL Classification: C53, E17, E37, F47

Suggested Citation

Carriero, Andrea and Clark, Todd E. and Marcellino, Massimiliano, Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions (September 21, 2020). FRB of Cleveland Working Paper No. 20-02R, Available at SSRN: https://ssrn.com/abstract=3520777 or http://dx.doi.org/10.2139/ssrn.3520777

Andrea Carriero

Queen Mary, University of London ( email )

Mile End Road
London, London E1 4NS
United Kingdom

Todd E. Clark (Contact Author)

Federal Reserve Bank of Cleveland ( email )

P.O. Box 6387
Cleveland, OH 44101
United States
216-579-2015 (Phone)

Massimiliano Marcellino

Bocconi University - Department of Economics ( email )

Via Gobbi 5
Milan, 20136
Italy

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

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