4 papers in the archive (2024–2026), average empirical rigor 5.4/10, average math complexity 7.9/10. Quadrant mix: Holy Grail ×3, Lab Rats ×1.
Works mostly in Risk Management & Tail Risk, Insurance & Actuarial Risk, Crypto Markets & DeFi, Reinforcement Learning for Trading.
Frequent co-authors: Tong Pu (1), Yiying Zhang (1), Wanting He (1), Wenyuan Li (1), Yang Liu (1), Xintao Ye (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 introduces an innovative framework for the periodic evaluation of defined-contribution pension funds. The performance of the pension fund is evaluated not only at retirement, but also within the interim periods. In contrast to the traditional literature, we set the dynamic risk measure as
Various financial market scenarios may cause heterogeneous risk assessments among analysts, which motivates the usage of the Generalized Risk Measure in Fadina et al. (2024, Finance and Stochastics). Effectively synthesizing these diverse assessments avoids over-relying on a single, potentially flaw
We introduce a novel class of systemic risk measures, the Vulnerability Conditional risk measures, which try to capture the “tail risk” of a risky position in scenarios where one or more market participants is experiencing financial distress. Various theoretical properties of Vulnerability Condition
In this paper, we study the risk sharing problem among multiple agents using Lambda Value-at-Risk as their preference functional, under heterogeneous beliefs, where beliefs are represented by several probability measures. We obtain semi-explicit formulas for the inf-convolution of multiple Lambda Va