3 papers in the archive (2023–2026), average empirical rigor 2.3/10, average math complexity 8.2/10. Quadrant mix: Lab Rats ×3.
Works mostly in Insurance & Actuarial Risk, Stochastic Control & Optimal Stopping, Portfolio Optimization, Risk Management & Tail Risk.
Frequent co-authors: Liyuan Lin (1), Jingzhen Liu abd Luyang Yu (1), Ziyue Shi (1), David Landriault (1), Xiangxin He (1), Ruodu Wang (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 the literature, insurance and reinsurance pricing is typically determined by a premium principle, characterized by a risk measure that reflects the policy seller’s risk attitude. Building on the work of Meyers (1980) and Chen et al. (2016), we propose a new performance-based variable premium sche
We study optimal reinsurance in the framework of stochastic game theory, in which there is an insurer and two reinsurers. A Stackelberg model is established to analyze the non-cooperative relationship between the insurer and reinsurers, where the insurer is considered as the follower and the reinsur
Portfolio diversification is a cornerstone of modern finance, while risk aversion is central to decision theory; both concepts are long-standing and foundational. We investigate their connections by studying how different forms of diversification correspond to notions of risk aversion. We focus on t