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Anand Deo · 2026-06-30
The paper proposes a mathematical method to automatically generate realistic financial stress scenarios instead of relying on manually chosen ones. Using large-deviations theory, it shows that conditional on a large loss, risk factors cluster around their most likely 'stressed' configurations, which lets the method extrapolate to extreme scenarios even when historical data has few or no examples of such stress. Tests on two financial network models show it reproduces the stressed loss distribution and diagnostics where standard generators produce no stressed samples.
Why it matters: Risk managers and analysts running stress tests might find this relevant because it offers a more systematic, less arbitrary way to identify dangerous configurations, particularly for rare tail events with little historical precedent. It could help avoid both missing genuinely risky scenarios and overweighting implausible ones.
⚠ Results come from numerical experiments on stylized financial network models, not live risk systems, and depend on the large-deviations assumptions holding for the risk-factor distribution.
Financial stress tests based on handpicked scenarios can mislead risk management by overlooking genuinely dangerous configurations or overemphasising shocks that are too implausible to be decision-relevant. We develop a systematic method for generating plausible stress scenarios for financial losses driven by exogenous risk factors. The method exploits a large-deviations principle: conditional on a large loss, the risk factors concentrate near the most likely stress configurations. We use this structure to define representative stress distributions and to extrapolate observed samples into more extreme scenarios while preserving the relative plausibility of stress mechanisms. As a result, the procedure can generate informative stress scenarios even when historical data contain few or no observations in the stressed regime. Numerical experiments on two financial network models show that the method recovers the stressed loss law and key stress diagnostics, including in settings where benchmark generators fail to generate any stressed samples.
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AI summary generated from the paper’s public abstract via arXiv; it may miss nuance — read the source before relying on it. Thank you to arXiv for its open-access interoperability; StockTools is not affiliated with arXiv, and all rights remain with the authors. Educational only, not financial advice.