SSRNRisk, Credit & Banking

When Backtests Lie: A Governance-Grade Validation Framework for Machine-Learning Value-at-Risk Models under Regime Shifts

The study shows that machine-learning VaR models passing standard backtests breach risk limits two to nine times worse in stressed regimes, revealing false comfort in current regulatory validation.