A pragmatic macroeconomic default risk adjustment in developing countries

Abstract

Background: The expected credit loss (ECL) framework of International Financial Reporting Standards Foundation (IFRS) 9 typically comprises three components: probability of default (PD), loss given default (LGD) and exposure at default (EAD). Among these, PD often lacks a systematic approach for incorporating macroeconomic dynamics, particularly in the developing economies.

Aim: This article proposes a novel methodology for dynamically adjusting PD using a macroeconomic scalar that integrates forward-looking information.

Setting: The proposed methodology is illustrated on datasets from Kenya and Mauritius to validate its applicability.

Method: The methodology consists of five steps: (1) research and planning; (2) data preparation; (3) model development; (4) calculation of the scalar; and (5) model validation. Comparative analysis is conducted using multiple regression, generalised linear models (Logit, Probit), and machine learning techniques such as neural networks, random forests, and gradient boosting. Model performance is assessed using key summary statistics and validation metrics.

Results: The proposed macroeconomic scalar effectively adjusted PD within the ECL model for the Kenya and Mauritius datasets. Each modelling approach contributed insights, demonstrating the scalar’s ability to improve ECL predictions.

Conclusion: Integrating a macroeconomic scalar into the ECL model offers a robust method for incorporating forward-looking information, improving PD accuracy that can account for uncertainty, volatility and sparse data characteristic of developing economies.

Contribution: This article provides a systematic approach for adjusting PD in ECL models using macroeconomic data offering a scalable solution. Additionally, we provide practical guidelines and step-by-step recommendations for practitioners seeking to implement macroeconomic adjustments in PD estimation.