3 papers in the archive (2024–2025), average empirical rigor 1.3/10, average math complexity 8.5/10. Quadrant mix: Lab Rats ×3.
Works mostly in Risk Management & Tail Risk, Insurance & Actuarial Risk.
Frequent co-authors: Mario Ghossoub (3), Ruodu Wang (3).
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 study risk sharing among agents with preferences modeled by heterogeneous distortion risk measures, who are not necessarily risk averse. Pareto optimality for agents using risk measures is often studied through the lens of inf-convolutions, because allocations that attain the inf-convolution are
In risk-sharing markets with aggregate uncertainty, characterizing Pareto-optimal allocations when agents might not be risk averse is a challenging task, and the literature has only provided limited explicit results thus far. In particular, Pareto optima in such a setting may not necessarily be como
We study Pareto-optimal risk sharing in economies with heterogeneous attitudes toward risk, where agents’ preferences are modeled by distortion risk measures. Building on comonotonic and counter-monotonic improvement results, we show that agents with similar attitudes optimally share risks comonoton