When Stress Tests Miss the Risk: Statistical Scenario Analysis for Financial Portfolios

2026-09-29

AI Summary

This study addresses the limitations of conventional stress testing, which often neglects risk factor dependencies and tends to underestimate portfolio risk through point estimation. We propose an adaptive conformal and kernel-based scenario analysis framework leveraging machine learning. By calibrating prediction intervals online and estimating conditional quantiles, this approach quantifies scenario return uncertainty while ensuring long-run empirical coverage guarantees. The proposed method overcomes the inherent shortcomings of traditional point estimates, significantly improving calibration accuracy and yielding sharper prediction intervals. Consequently, it effectively identifies latent tail risks in portfolios that would otherwise appear safe under standard stress testing procedures.

Abstract

Scenario analysis is widely used to stress test financial portfolios, yet conventional approaches often summarize scenario gains using point estimates that overlook dependence between stressed and unstressed risk factors. We develop a machine learning framework for quantifying uncertainty in realized next-day scenario gains through prediction intervals whose widths can be calibrated online. Adaptive conformal scenario analysis (ACSA) calibrates scenario-specific quantile predictions and provides a long-run empirical coverage guarantee over realized scenarios. Our main method, kernel scenario analysis (KSA), estimates scenario-conditional quantiles directly from the specified stress and current market information. KSA can also be combined with ACSA to obtain online-calibrated prediction intervals. In experiments designed to reflect real-world markets, conventional scenario analysis can substantially understate risk, including for portfolios that appear safe under standard stress tests. Our proposed methods achieve better calibration and sharper intervals than empirical-quantile baselines. Overall, the framework moves scenario analysis beyond point estimates by quantifying predictive uncertainty and providing tools for statistically validating scenario gains.

Problem

• Scenario Analysis
• Stress Testing
• Financial Portfolios
• Risk Underestimation
• Uncertainty Quantification

Innovation

• Scenario Analysis
• Conformal Prediction
• Prediction Intervals
• Kernel Quantile Estimation
• Online Calibration