An extendable, integrated, and dynamic approach to forecasting and stress-testing credit risk

2026-06-17

AI Summary

This study addresses the limitations of traditional stress testing approaches, which often decouple loan origination dynamics and neglect the correlation structure among risk indicators, thereby failing to capture the evolving nature of credit risk. To overcome these shortcomings, the paper proposes an integrated, scalable dynamic stress testing framework that, for the first time, jointly models loan issuance and credit risk prediction. The framework employs a multi-state probabilistic model to simulate loan cash flows and embeds macroeconomic stress scenarios directly within Monte Carlo simulations. It allows risk parameters to adjust dynamically in response to both macroeconomic and microeconomic variables and explicitly incorporates the interdependencies among key risk metrics. This approach significantly enhances the realism, flexibility, and forward-looking capability of stress testing, enabling dynamic forecasts of portfolio-level default and loss rates under a wide range of scenarios.

Abstract

An integrated and extendable approach for stress-testing loan portfolios is presented, which includes both a loan production component and a credit risk component. In this approach, we simulate a completed portfolio using realistic loan parameters and distributional assumptions. Thereafter, we generate the uncertain cash flow history of these loans within a multistate probabilistic framework. We illustrate our approach using a simulation-based study, though the approach can be fit to real-world data. Such a simulation-based approach is ideal for stress-testing since it allows for evaluating a range of conditions. From these completed loans, we compute portfolio-level credit risk metrics, e.g., default and loss rates. Stress scenarios are introduced by varying the loan parameters accordingly within a broader Monte Carlo setup, thereby resulting in a range of portfolios. A classical approach to stress-testing does not typically integrate loan production or embed the correlation structure amongst risk metrics. In our approach, we integrate the forecasting of risk metrics with receipt-generation. Given data, the loan parameters within our extendable approach can be dynamically modelled as functions of input variables using any applicable technique. Overall, our approach can render predictions that are more dynamic and flexibly tuned, which can enhance stress-testing practices within any bank.

Problem

• credit risk
• stress-testing
• loan portfolio
• risk metrics
• correlation structure

Innovation

• integrated stress-testing
• dynamic credit risk modeling
• loan production simulation
• multistate probabilistic framework
• extendable Monte Carlo approach