Financial Shocks and the Output Growth Distribution

This paper studies how financial shocks shape the distribution of output growth by introducing a quantile-augmented vector autoregression (QAVAR), which integrates quantile regressions into a structural VAR framework. The QAVAR preserves standard shock identification while delivering flexible, nonparametric forecasts of conditional moments and tail risk measures for gross domestic product (GDP). Applying the model to financial conditions and credit spread shocks, we find that adverse financial shocks worsen the downside risk to GDP growth significantly, while the median and upper percentiles respond more moderately. This underscores the importance of nonlinearities and heterogeneous tail dynamics in assessing macro-financial risks.