DP17512 Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions
A rapidly growing body of research has examined tail risks in macroeconomic outcomes, commonly using 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. 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). We show that Bayesian vector autoregressions (BVARs) with stochastic volatility are able to capture tail risks in macroeconomic forecast distributions and outcomes. Even though the 1-step-ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, forecasts of downside risks to output growth are more variable than upside risks, and the reverse applies in the case of inflation and unemployment. Overall, the BVAR models perform comparably to quantile regression for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis.