Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

Andrea Carriero, Todd Clark and Massimiliano Marcellino

No 20-02R, Working Papers from Federal Reserve Bank of Cleveland

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: downside risk; forecasting; asymmetries (search for similar items in EconPapers)
JEL-codes: C53 E17 E37 F47 (search for similar items in EconPapers)
Pages: 79
Date: 2020-01-16, Revised 2020-09-22
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac, nep-ore and nep-rmg
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Citations: View citations in EconPapers (24)

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Related works:
Journal Article: Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions (2024)
Working Paper: Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions (2022)
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DOI: 10.26509/frbc-wp-202002r

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