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.
This paper explores the critical importance of the interest rate risk in the banking book (IRRBB) regulations set forth by the Basel Committee on Banking Supervision. It emphasizes the need for financial institutions to develop robust models for forecasting demand deposit balances while adhering to regulatory guidelines. To ensure these models meet the stringent requirements of risk model validation, we implement comprehensive validation techniques that assess predictive accuracy and robustness. Using a random sample of demand deposit balances in the Mexican banking sector, our research contrasts the forecasting results to robustly and efficiently construct survival rates and attrition curves (harvests) through traditional time-series methods and the use of artificial intelligence algorithms. While the Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroscedasticity models showed the best goodness-of-fit results, time-series models lack the flexibility and robustness needed to capture the complexities of demand deposit balance behavior, especially in negative interest rate scenarios. In contrast, the use of machine learning techniques significantly improves predictive accuracy. The results demonstrate that our long artificial neural network-long short-term memory and random forest models improve the prediction results for demand deposit balances, allowing us to efficiently construct attrition curves and survival rates, providing better risk management tools for financial institutions facing the challenges posed by the IRRBB.
The Fundamental Review of the Trading Book encourages financial institutions to shift from the value-at-risk (VaR) to the expected shortfall (ES) risk measure when measuring market risk capital. This paper examines the application of exceedancebased validation (or "backtesting") methods, commonly used for VaR model validation, to the validation of ES models. The examined approach includes finding the quantile value corresponding to the ES for four different estimation methods: an analytical delta-normal approach, an analytical generalized Pareto distribution-based approach, numerical Monte Carlo simulation and nonparametric historical simulation. The paper also investigates the stability of this quantile and proposes an adjustment to the traditional backtesting approaches that helps to accommodate an unstable quantile. The application of the approach is illustrated using real-world Baltic equity index (OMXBBGI) returns data for three different confidence levels: 90%, 95% and 97.5%. Our findings show that the direct application of exceedance-based methods to the validation of ES models is possible, even in the case of an unstable ES quantile.
