Financial Conditions and U.S. Macro Tail Risk

How do financial conditions shape U.S. macroeconomic tail risk, and what is their incremental forecasting value beyond standard macroeconomic predictors? Using quarterly U.S. data from 1971Q1 to 2026Q1, this paper compares macro-only and macro-financial Bayesian VARs. First-lag financial-conditions coefficients are negative for GDP growth and positive for unemployment, with 95% posterior credible intervals excluding zero; second-lag coefficients have opposite signs. Adding financial conditions to the Gaussian VAR raises predicted probabilities of negative GDP growth by 1.5 and 2.5 percentage points at four- and eight-quarter horizons and lowers GDP tail quantiles. However, out-of-sample tests do not establish a general forecast-accuracy improvement. Student-t forecast innovations, simulated with the same Gaussian posterior parameter draws, produce heavier predictive tails. Their positive GDP log-score differences reverse when 2020Q1–2021Q4 forecast targets are excluded, while CRPS favors Gaussian innovations in several comparisons. Financial conditions change measured tail risk without consistently improving forecast accuracy.