Many studies using quantile regressions (QRs) have found that downside risk to output growth varies more than upside risk. We show that Bayesian vector autoregressions (BVARs) with stochastic volatility are able to capture tail risks in forecast distributions. Even though the one-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, BVAR models perform comparably to QR for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis.
Using supervisory data on small and midsized nonfinancial enterprises (SMEs), we find that those SMEs with higher leverage faced tighter constraints in accessing bank credit after the COVID-19 outbreak in spring 2020. Specifically, SMEs with higher pre-COVID leverage obtained a smaller volume of new loans and had to pay a higher spread on them during the pandemic period. Consistent with an inward shift in loan supply, these effects were concentrated in loans originated by banks with below-median capital buffers. Highly levered SMEs that relied on low-capital large banks for funding before the pandemic were not able to substitute to other sources of debt financing and thus experienced more of a reduction in total debt as well as a decline in investment and employment. On the other hand, the unprecedented public support, especially the Paycheck Protection Program (PPP), mitigated the adverse real effect stemming from bank credit constraints.
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