Abstract: Conventional credit risk models understate tail risk by centering on mean default probabilities and neglecting distributional and sectoral heterogeneity. We propose a Quantile Probability of Default (QPD) framework based on unconditional quantile regressions estimated on flow default rates from five million non-financial firms across nine countries, conditioned on macro- and sectoral scenario covariates standard in stress testing. The tail exhibits three- to five-fold stronger sensitivity than at the median, revealing non-linearities and asymmetric sectoral propagation of credit risk. We validate the performance of our model across crisis periods and benchmark models to confirm the framework’s robustness and prudential efficiency. Under the European Central Banks’s 2025 increasing geopolitical and trade tensions scenario, the QPD identifies higher tail vulnerabilities in construction, trade, hospitality, and real estate. The framework embeds distributional estimation into stress testing, advancing scenario-based assessment of sectoral credit risk for policy and prudential applications.

https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp3207~4ec5f4abf6.en.pdf