3 papers in the archive (2023–2026), average empirical rigor 4/10, average math complexity 7.7/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Risk Management & Tail Risk, Options & Derivatives Pricing.
Frequent co-authors: Stéphane Crépey (2), Samuel Drapeau (1), Mekonnen Tadese (1), Nikeethan Selvaratnam (1), Clément Fernandes (1), Wojciech Pieczynski (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%).
Predicting future operational risk losses gives rise to a significant challenge due to the heterogeneous and time-dependent structures present in real-world data. Furthermore, stress test exercises require examining the relationship with operational losses. To capture such relationship, we propose t
For vanilla derivatives that constitute the bulk of investment banks’ hedging portfolios, central clearing through central counterparties (CCPs) has become hegemonic. A key mandate of a CCP is to provide an efficient and proper clearing member default resolution procedure. When a clearing member def
Based on supermodularity ordering properties, we show that convex risk measures of credit losses are nondecreasing w.r.t. credit-credit and, in a wrong-way risk setup, credit-market, covariances of elliptically distributed latent factors. These results support the use of such setups for computing cr