5 papers in the archive (2025–2026), average empirical rigor 2/10, average math complexity 8.4/10. Quadrant mix: Lab Rats ×5.
Works mostly in Risk Management & Tail Risk, Insurance & Actuarial Risk, Portfolio Optimization, Factor Investing & Asset Pricing.
Frequent co-authors: Taizhong Hu (4), Yuting Su (2), Yuyu Chen (1), Seva Shneer (1), Keyi Zeng (1), Shuyu Gong (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%).
This paper proposes a novel class of generalized Expected-Shortfall (ES) norms constructed via distortion risk measures, establishing a unified analytical framework for risk quantification. The proposed norms extend conventional ES methodology by incorporating flexible distortion functions. Specific
This paper introduces the Lambda extension of the Rényi entropic value-at-risk ($Λ$-EVaR), a novel family of risk measures that unifies the flexible confidence level structure of the $Λ$-framework with the higher-moment sensitivity of EVaR. We define $Λ$-EVaR, establish its foundational properties i
In recent years, stochastic dominance for independent and identically distributed (iid) infinite-mean random variables has received considerable attention. The literature has identified several classes of distributions of nonnegative random variables that encompass many common heavy-tailed distribut
The extreme cases of risk measures, when considered within the context of distributional ambiguity, provide significant guidance for practitioners specializing in risk management of quantitative finance and insurance. In contrast to the findings of preceding studies, we focus on the study of extreme
In this paper, we establish a sufficient condition to compare linear combinations of independent and identically distributed (iid) infinite-mean random variables under usual stochastic order. We introduce a new class of distributions that includes many commonly used heavy-tailed distributions and sh