欧洲部门企业违约的分位数概率模型

A quantile probability model for sectoral corporate defaults in EuropePaul Konietschke, Julian Metzler,Aurea Ponte Marques Abstract Conventional credit risk models understate tail risk by centering on mean default prob-abilities and neglecting distributional and sectoral heterogeneity.We propose a QuantileProbability of Default (QPD) framework based on unconditional quantile regressions es-timated 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 tailexhibits three- to five-fold stronger sensitivity than at the median, revealing non-linearitiesand asymmetric sectoral propagation of credit risk.We validate the performance of ourmodel across crisis periods and benchmark models to confirm the framework’s robustness JEL codes:C21, C54, D22, G21, G32Keywords:Firm dynamics, Non-linearity, Probability of default, Stress testing, Trade tension Non-Technical Summary This paper introduces a new framework to measure sector-level corporate credit risk in theeuro area and to improve its integration into banking stress-testing.It proposes a “quantileprobability of default” (QPD) model that uses quantile regression techniques to estimate the Empirically, the paper assembles a new panel of sectoral flow default rates using firm-levelbalance sheet information from the Orbis database for roughly five million non-financial firmsin nine euro area countries over 1999–2023. Defaults are identified using a balance-sheet-basedcriterion `a la Gourinchas et al. (2020), whereby a firm is classified as defaulted once internalcash flow fails to cover financial expenses in two consecutive years, so that the measure cap- The QPD model relates these sectoral default rates to a set of macro-financial variables thatare chosen to coincide with those used in euro-area stress-testing scenarios: real sectoral GrossValue Added (GVA), unemployment, short- and long-term interest rates, the term spread, eq-uity prices and property prices. Methodologically, the paper adopts an unconditional quantileregression framework with sector–country fixed effects, implemented via recentered influence A series of validation exercises demonstrates that the QPD model satisfies key criteria forstress-testing use.In-sample, euro-area aggregate PDs, obtained by GVA-weighting sectoralQPD estimates, track realised default rates closely and reproduce the spikes associated withthe global financial crisis and the euro area sovereign debt crisis. Realised defaults tend to liebetween the model’s 50th and 70th percentiles in tranquil periods and move closer to higher The paper’s main scenario application is an adverse trade-tension shock aligned with the se-vere macro-financial scenario in the European Central Banks’s (ECB) June 2025 Broad Macroe-conomic Projection Exercise and the 2025 EU-wide stress test. The scenario features a sizeableand persistent increase in US tariffs on euro-area exports, symmetric EU retaliation, and persis-tently high trade policy uncertainty, which together depress global investment, euro-area exportsand domestic demand. Feeding the corresponding paths for GDP, unemployment, interest rates As a background, the QPD framework has served as the ECB’s benchmark model for as-sessing the credit risk of non-financial corporations since 2022, supporting the 2023 and 2025EU-wide stress tests. Its implementation followed detailed scrutiny and approval by designatedand competent authorities and is embedded within a regular validation and governance struc-ture. The framework extends the ECB’s ongoing programme to advance top-down credit riskmodelling, initiated by the Financial Stability Committee’s Working Group on Stress Testing From a policy standpoint, the QPD framework delivers three main contributions.First,by modelling the full distribution of sectoral default risk, it allows supervisors and centralbanks to trace how vulnerabilities evolve not only on average but also in the tail, which is cru-cial for assessing financial stability under severe but plausible scenarios. Second, the country-and sector-level granularity reveals pockets of risk that are obscured in aggregate PD measures,thereby supporting a more targeted calibration of macroprudential instruments and supervisory 1Introduction The accurate assessment of corporate default risk is crucial for financial stability and pru-dent risk management.Traditional credit risk models, which often assume linear responsesto macroeconomic shocks, have shown significant limitations in capturing the true extent ofvulnerabilities exposed by geopolitical tensions, trade wars, and abrupt economic disruptions.Recent episodes, such as the escalation of global trade conflicts and the COVID-19 pandemic, The motivation for this study arises from the growing recognition that linear stress testingframeworks systematically underestimate tail risks and fail to capture the cross-sectoral h