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 has relied on quantile regression methods to estimate tail risks. In
this paper 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
featuring a common volatility factor that is a function of past financial conditions. Even though the conditional predictive distributions from the VAR models
are symmetric, our estimated models featuring time-varying volatility yield more
time variation in downside risk as compared to upside risk—a feature highlighted in other work that has advocated for quantile regression methods or focused
on asymmetric conditional distributions. Overall, the BVAR models perform
comparably to quantile regression for estimating tail risks, with, in addition,
some gains in standard point and density forecasts.