Validating bank risk models under trade war stress: a framework for adaptive stress testing with AI-driven calibration and cross-industry applications

Krishan Kumar Sharma

Abstract

The escalation of trade tensions and tariff shocks has created persistent uncertainty for financial institutions by directly impacting the integrity of traditional bank risk models. Conventional frameworks often fail to capture the nonlinear and sector-specific effects of tariffs on credit, market and operational risk, which results in misestimated portfolio losses and distorted capital allocation. This paper develops a comprehensive methodology, focusing on the validation and enhancement of risk models specifically under trade war conditions. It introduces three key dimensions: embedding explicit tariff shocks into stress scenarios; applying granular sector-level calibration; and adopting adaptive governance with artificial-intelligence- driven recalibration and overlays. Using local projection methods with robust alternative specifications and controls, the study quantifies how tariffs are transmitted through inflation, interest rates and gross domestic product. It then maps these effects into core model parameters such as the probability of default, loss given default, preprovision net revenue and market risk metrics. Empirical tests, including the Diebold–Mariano test, demonstrate that tariff-augmented models significantly outperform traditional specifications in out-of-sample forecasting. A case study of a US regional bank further illustrates the practical application of this framework in recalibrating Current Expected Credit Losses and Comprehensive Capital Analysis and Review models. By integrating empirical stress testing with robustness checks, artificial intelligence tools and conservative governance as well as a reproducible calibration protocol, the framework strengthens resilience, supports regulatory compliance and ensures forward-looking model risk management under sustained geopolitical uncertainty.