3 papers in the archive (2023–2026), average empirical rigor 7.8/10, average math complexity 5.3/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in Risk Management & Tail Risk.
Frequent co-authors: Arno Botha (3), Marcel Muller (2), Mohammed Gabru (1), Esmerelda Oberholzer (1), Riaan de Jongh (1), Henko Crewe (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
The estimation of marginal loan write-off probabilities is a non-trivial task when modelling the loss given default (LGD) risk parameter in credit risk. We explore two types of survival models in estimating the overall write-off probability over default spell time, where these probabilities form the
The use of run-off triangles (ROTs) is a common industry practice in estimating the loss given default (LGD) risk parameter when predicting credit losses in banking. We benchmark this industry practice using credit card data against a more sophisticated (though classical) regression-based approach,
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 proba