Journal of Credit Risk
ISSN:
1755-9723 (online)
Editor-in-chief: Linda Allen and Jens Hilscher
Need to know
- This paper introduces a new decomposition approach for exposure at default.
- Idiosyncratic and macro-sensitive components are estimated.
- The precision of the model is assessed on stress period, including COVID-19 data.
- The approach reduces forecast errors, mainly with the use of gradient boosting.
Abstract
We propose a novel method of including macroeconomic variables in exposure at default models, which satisfies all expectations connected to International Financial Reporting Standard 9 requirements. In addition, it is intuitive and transparently transforms the situation in the credit environment into expected loss values. We propose a decomposition approach that separates the contract-based variables from the macroeconomic indicators. Using various estimation methods, we build a set of models that combine idiosyncratic information gathered at the exposure level with systematic indicators collected quarterly. We test our predictions on out-of-time data, which includes the Covid-19 pandemic period, and find that our decomposition outperforms benchmarks in terms of selected forecast quality metrics. The proposed solution allows risk managers to adjust capital levels, making their financial institution more competitive in the market.
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