3 papers in the archive (2025–2026), average empirical rigor 6.3/10, average math complexity 6.8/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Insurance & Actuarial Risk, Risk Management & Tail Risk, Machine Learning for Market Forecasting.
Frequent co-authors: Olivier Lopez (3), Antoine Heranval (1), Didier Ngatcha (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%).
In this paper, we address the problem of providing insurance protection against heavy-tailed losses, for which the expected loss may not even be finite. The product we study is based on a combination of traditional insurance up to a given limit and a parametric (or index-based) cover for larger loss
According to the United Nations Office for Disaster Risk Reduction (2025), the average annual cost of natural catastrophes increased from 70–80 billion USD between 1970 and 2000 to 180–200 billion USD between 2001 and 2020. Reports from organizations such as the IFOA and the WWF highlight the need
Index insurance is often proposed to reduce protection gaps, especially for emerging risks. Unlike traditional insurance, it bases compensation on a measurable index, enabling faster payouts and lower claim management costs. This approach benefits both policyholders, through quick payments, and insu