3 papers in the archive (2025–2026), average empirical rigor 3.8/10, average math complexity 7.8/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Stochastic Control & Optimal Stopping, Insurance & Actuarial Risk, Risk Management & Tail Risk, Commodities & Energy Markets.
Frequent co-authors: Nora Muler (2), Pablo Azcue (1), Jan Beirlant (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%).
We provide a survey of how techniques developed for the modelling of extremes naturally matter in insurance, and how they need to and can be adapted for the insurance applications. Topics covered include truncation, tempering, censoring and regression techniques. The discussed techniques are illustr
We study optimal dividend strategies for an insurance company facing natural catastrophe claims, anticipating the arrival of a climate tipping point after which the claim intensity and/or the claim size distribution of the underlying risks deteriorates irreversibly. Extending earlier literature base
Achieving net-zero carbon emissions requires a transformation of energy systems, industrial processes, and consumption patterns. In particular, a transition towards that goal involves a gradual reduction of excess carbon emissions that are not essential for the well-functioning of society. In this p