3 papers in the archive (2024–2026), average empirical rigor 4.5/10, average math complexity 8.2/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Insurance & Actuarial Risk, Risk Management & Tail Risk, Factor Investing & Asset Pricing.
Frequent co-authors: Andrew Fleck (1), Yang Shen (1), Nawaf Mohammed (1), Jinghui Chen (1), X. Sheldon Lin (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%).
Measuring the contribution of a bank or an insurance company to overall systemic risk is a key concern, particularly in the aftermath of the 2007–2009 financial crisis and the 2020 downturn. In this paper, we derive worst-case and best-case bounds for the marginal expected shortfall (MES) – a key
Nowadays insurers have to account for potentially complex dependence between risks. In the field of loss reserving, there are many parametric and non-parametric models attempting to capture dependence between business lines. One common approach has been to use additive background risk models (ABRMs)
Multiplicative background risk models (MBRMs) provide a tractable framework for describing dependent risks. We construct a class of MBRMs with Pareto margins by combining Marshall–Olkin exponential and additive gamma risk factor structures, each allowed to incorporate idiosyncratic and systemic bac