A Methodology to Measure Impacts of Scenarios Through Expected Credit Losses

Paper: arXiv 2602.01361

Authors: Mahmood Alaghmandan, Meghal Arora, Olga Streltchenko

Abstract

In this paper, we present a methodology for measuring the impact of scenarios on the expected losses of exposures by leveraging the existing provisioning infrastructure within financial institutions, where scenario effects are captured through changes in probabilities of default. We then describe how to design and implement a scenario test where risk drivers are given for standardized groupings of exposures, and the groupings are defined based on common features of the exposures. The methodology presented served as a theoretical foundation for the standardized climate scenario exercise conducted in 2024 by the Office of the Superintendent of Financial Institutions of Canada and Quebec’s Autorite des Marches Financiers.

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper provides a formal theoretical framework leveraging IFRS 9/CECL expected credit loss infrastructure for macro/climate scenario stress testing, utilizing survival probability decompositions and Frye-Jacobs transformations. While mathematically clear and practical for regulatory design, it focuses primarily on methodology and standardized formulations rather than empirical backtesting on actual portfolio data.

Research Flowchart

  flowchart TD
    A[Goal: Measure Scenario Impacts on Expected Credit Losses] --> B[Input: Scenario Risk Drivers & Exposure Groupings]
    B --> C[Leverage Existing Provisioning Infrastructure]
    C --> D[Adjust Probabilities of Default based on Risk Drivers]
    D --> E[Calculate Impacted Expected Credit Losses]
    E --> F[Outcome: Standardized Climate Scenario Framework OSFI / AMF 2024]