What question did this study set out to answer?

The study aims to compare different methodologies for modeling the probability of default under IFRS 9, focusing on predictive accuracy.

March 5, 2026International Journal of Financial StudiesOpen Access

Modeling the Probability of Default Term Structure Using Different Methodologies Under IFRS 9

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The study aims to compare different methodologies for modeling the probability of default under IFRS 9, focusing on predictive accuracy.
Utilized Cox proportional hazard, Extended Cox proportional hazard, and Random Boosting Forest methods.
Applied Kaplan–Meier model as a benchmark for comparison.
Analyzed publicly available mortgage loan portfolio data from Freddie Mac.
Evaluated models using different goodness-of-fit measures and predictive accuracy metrics.
Extended Cox PH and RBF models demonstrated better predictive accuracy indicated by higher C-index.
Cox PH model showed a more efficient fit with a stable hazard trajectory.
Extended models had increased complexity and risk of overfitting.

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Implication

This research compares three methodologies for predicting loan default probability in mortgage portfolios, indicating best practices for banks under IFRS 9.

Cite This Study

Moremoholo et al. (2026) studied this question.

synapsesocial.com/papers/69a91e1fd6127c7a504c1c5c https://doi.org/10.3390/ijfs14030062

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Implication

This research compares three methodologies for predicting loan default probability in mortgage portfolios, indicating best practices for banks under IFRS 9.

Modeling the Probability of Default Term Structure Using Different Methodologies Under IFRS 9

Key Points

Discussion

Implication

Cite This Study

Modeling the Probability of Default Term Structure Using Different Methodologies Under IFRS 9

Key Points

Discussion

Implication

Cite This Study

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