Paper: arXiv 2303.03080

Authors: Arno Botha, Esmerelda Oberholzer, Janette Larney, Riaan de Jongh

Abstract

The IFRS 9 accounting standard requires the prediction of credit deterioration in financial instruments, i.e., significant increases in credit risk (SICR). However, the definition of such a SICR-event is inherently ambiguous, given its current reliance on evaluating the change in the estimated probability of default (PD) against some arbitrary threshold. We examine the shortcomings of this PD-comparison approach and propose an alternative framework for generating SICR-definitions based on three parameters: delinquency, stickiness, and the outcome period. Having varied these framework parameters, we obtain 27 unique SICR-definitions and fit logistic regression models accordingly using rich South African mortgage and macroeconomic data. For each definition and corresponding model, the resulting SICR-rates are analysed at the portfolio-level on their stability over time and their responsiveness to economic downturns. At the account-level, we compare both the accuracy and dynamicity of the SICR-predictions, and discover several interesting trends and trade-offs. These results can help any bank with appropriately setting the three framework parameters in defining SICR-events for prediction purposes. We demonstrate this process by comparing the best-performing SICR-model to the PD-comparison approach, and show the latter’s inferiority as an early-warning system. Our work can therefore guide the formulation, modelling, and testing of any SICR-definition, thereby promoting the timeous recognition of credit losses; the main imperative of IFRS 9.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper tackles a critical practical problem in IFRS 9 implementation by moving away from standard PD-threshold comparisons to a novel 3-parameter framework. It evaluates 27 SICR definitions using an extensive empirical mortgage dataset with robust statistical analysis, prioritizing practical credit risk monitoring over complex mathematical derivations.

Research Flowchart

  flowchart TD
    A[Research Goal: Address Ambiguity in IFRS 9 SICR Definition] --> B[Data Inputs: South African Mortgage & Macroeconomic Data]
    B --> C[Proposed Framework: 3 Parameters<br/>Delinquency, Stickiness, Outcome Period]
    C --> D[Generated 27 Unique SICR Definitions]
    D --> E[Fitted Logistic Regression Models for Each Definition]
    E --> F[Analysis & Evaluation]
    F --> G[Portfolio Level: Stability & Responsiveness]
    F --> H[Account Level: Accuracy & Dynamicity]
    G & H --> I[Outcome: Proposed Framework Superior to Standard PD-Comparison as Early-Warning System]