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<p>This study provides a structured approach for integrating macroeconomic conditions into ECL modeling under IFRS 9, particularly by adjusting the probability of default (PD). Since IFRS 9 mandates incorporating forward-looking information (FLI) into ECL calculations, selecting relevant macroeconomic factors is crucial for loan-providing institutions. A major part of the study investigates macroeconomic determinants of corporate loan default rates (DR) and their role in the calculation of expected credit loss (ECL) and PD. The paper contributes to existing research by examining a broad set of explanatory variables. These include financial market conditions, activity indicators, stock and commodity prices, indebtedness and monetary conditions. Using Czech data (2002Q4–2024Q4), this study identifies key DR drivers not only for the aggregate loan portfolio, but also across various loan portfolio subsegments, including economic sectors, loan types, collateral types, and bank sizes. The findings reveal that on the aggregate level, the most important drivers of the corporate default rate are the output gap and the unemployment rate. The relevant macroeconomic drivers of corporate DR significantly vary across subsegments. Finally, the paper demonstrates the incorporation of forecasted economic development into the calculation of PD by constructing a systematic risk factor Z under the Vasicek approach.</p>

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Incorporating macroeconomic conditions into corporate probability of default under IFRS 9

  • 期刊论文

  • 发表日期：

    2025年7月22日

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  • Ján Boháčik

摘要

This study provides a structured approach for integrating macroeconomic conditions into ECL modeling under IFRS 9, particularly by adjusting the probability of default (PD). Since IFRS 9 mandates incorporating forward-looking information (FLI) into ECL calculations, selecting relevant macroeconomic factors is crucial for loan-providing institutions. A major part of the study investigates macroeconomic determinants of corporate loan default rates (DR) and their role in the calculation of expected credit loss (ECL) and PD. The paper contributes to existing research by examining a broad set of explanatory variables. These include financial market conditions, activity indicators, stock and commodity prices, indebtedness and monetary conditions. Using Czech data (2002Q4–2024Q4), this study identifies key DR drivers not only for the aggregate loan portfolio, but also across various loan portfolio subsegments, including economic sectors, loan types, collateral types, and bank sizes. The findings reveal that on the aggregate level, the most important drivers of the corporate default rate are the output gap and the unemployment rate. The relevant macroeconomic drivers of corporate DR significantly vary across subsegments. Finally, the paper demonstrates the incorporation of forecasted economic development into the calculation of PD by constructing a systematic risk factor Z under the Vasicek approach.