4 papers in the archive (2024–2026), average empirical rigor 5.5/10, average math complexity 7.8/10. Quadrant mix: Holy Grail ×2, Lab Rats ×2.
Works mostly in Risk Management & Tail Risk, Machine Learning for Market Forecasting, Volatility Modeling & Forecasting, Insurance & Actuarial Risk.
Frequent co-authors: Cathy W. S. Chen (2), Wei-Hsuan Shau (1), Marius Hofert (1), Haruki Tsunekawa (1), Edward M. H. 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%).
Capital allocation is a procedure for quantifying the contribution of each source of risk to aggregated risk. The gradient allocation rule, also known as the Euler principle, is a prevalent rule of capital allocation under which the allocated capital captures the diversification benefit of the margi
This research incorporates realized volatility and overnight information into risk models, wherein the overnight return often contributes significantly to the total return volatility. Extending a semi-parametric regression model based on asymmetric Laplace distribution, we propose a family of RES-CA
Dependence among multiple lifetimes is a key factor for pricing and evaluating the risk of joint life insurance products. The dependence structure can be exposed to model uncertainty when available data and information are limited. We address robust pricing and risk evaluation of joint life insuranc
The classical tail dependence coefficient (TDC) may fail to capture non-exchangeable features of tail dependence due to its restrictive focus on the diagonal of the underlying copula. To address this limitation, the framework of path-based maximal tail dependence has been proposed, where a path of m