4 papers in the archive (2025–2026), average empirical rigor 1.9/10, average math complexity 8.4/10. Quadrant mix: Lab Rats ×4.
Works mostly in Insurance & Actuarial Risk, Risk Management & Tail Risk.
Frequent co-authors: Mario Ghossoub (4), Michael B. Zhu (1), Bin Li (1), Benxuan Shi (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 consider an insurance market with hidden information, where the agent’s type is private information and is drawn from an arbitrary type space. We study implementability of a collection of retention functions, namely, how to select premium schedules so that the resulting menu of contracts is incen
We study a monopolistic insurance market with hidden information, where the agent’s type $θ$ is private information that is unobservable to the insurer, and it is drawn from a continuum of types. The hidden type affects both the loss distribution and the risk attitude of the agent. Within this frame
We consider a model of a reinsurance market consisting of multiple insurers on the demand side and multiple reinsurers on the supply side, thereby providing a unifying framework and extension of the recent literature on optimality and equilibria in reinsurance markets. Each insurer has preferences r
We study Stackelberg Equilibria (Bowley optima) in a monopolistic centralized sequential-move insurance market, with a profit-maximizing insurer who sets premia using a distortion premium principle, and a single policyholder who seeks to minimize a distortion risk measure. We show that equilibria ar