Generating Plausible Stress Scenarios via Large Deviations

2026-06-30

AI Summary

This study addresses a critical limitation of traditional financial stress testing—its reliance on subjectively designed scenarios that often overlook plausible high-risk events or introduce implausible shocks. For the first time, large deviation theory is integrated into stress testing to systematically generate extreme yet realistic stress scenarios by identifying the most likely configurations of exogenous risk factors that lead to severe losses. This approach overcomes the scarcity of historical extreme observations by combining conditional concentration analysis, probabilistic modeling of risk factor distributions, and extrapolation techniques. The method robustly reproduces stress loss distributions and key diagnostic metrics across two distinct financial network models, demonstrating consistent effectiveness even in regimes where conventional approaches completely fail.

Abstract

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.

Problem

• stress testing
• plausible scenarios
• large deviations
• financial risk
• extreme events

Innovation

• large deviations
• stress testing
• plausible scenarios
• risk factors
• financial networks