3 papers in the archive (2025–2026), average empirical rigor 3.7/10, average math complexity 8/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Risk Management & Tail Risk, Insurance & Actuarial Risk, Portfolio Optimization.
Frequent co-authors: Asmerilda Hitaj (1), Elisa Mastrogiacomo (1), Marcelo Righi (1), Linus Wunderlich (1), Akif Ince (1), Marlon Moresco (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%).
Lambda quantiles, originally introduced as lambda value at risk, generalise the classical value at risk by allowing for a variable confidence level. This work presents efficient algorithms for computing lambda quantiles and demonstrates their application in portfolio optimisation. We first develop a
This paper develops an axiomatic framework for ranking metrics, a general class of functionals for evaluating and ordering financial or insurance positions. Unlike traditional risk-adjusted performance measures-such as the Sharpe ratio, RAROC, or Omega-that express reward per unit of risk, ranking m
We provide a constructive way of defining new elicitable risk measures that are characterised by a multiplicative scoring function. We show that depending on the choice of the scoring function’s components, the resulting risk measure possesses properties such as monotonicity, translation invariance,