3 papers in the archive (2025–2026), average empirical rigor 3.7/10, average math complexity 8.7/10. Quadrant mix: Lab Rats ×3.
Works mostly in Risk Management & Tail Risk, Portfolio Optimization, Insurance & Actuarial Risk.
Frequent co-authors: Peng Liu (2), Carole Bernard (2), Yi Xia (1), Jinghui Chen (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 study the worst-case values of distortion riskmetrics over Bregman–Wasserstein balls around a reference distribution. Unlike the squared $2$-Wasserstein distance, Bregman–Wasserstein divergences can penalize upward and downward deviations differently. Our main result applies to generalized dist
Recent studies have highlighted the significance of higher-order moments - such as coskewness - in portfolio optimization within the financial domain. This paper extends that focus to the field of actuarial science by examining the impact of these moments on key actuarial applications. In the first
We establish sharp upper and lower bounds for distortion risk metrics under distributional uncertainty. The uncertainty sets are characterized by four key features of the underlying distribution: mean, variance, unimodality, and Wasserstein distance to a reference distribution. We first examine very