3 papers in the archive (2023–2025), average empirical rigor 1.3/10, average math complexity 8.7/10. Quadrant mix: Lab Rats ×3.
Works mostly in Risk Management & Tail Risk.
Frequent co-authors: Silvana M. Pesenti (2), Mélina Mailhot (1), Marcelo Righi (1), Eduardo Horta (1), Akif Ince (1), Ilaria Peri (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 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,
We introduce a framework for quantifying propagation of uncertainty arising in a dynamic setting. Specifically, we define dynamic uncertainty sets designed explicitly for discrete stochastic processes over a finite time horizon. These dynamic uncertainty sets capture the uncertainty surrounding stoc
We introduce the concept of set risk measures (SRMs), which are real-valued maps defined on the space of all non-empty, closed, and bounded sets of almost surely bounded random variables. Traditional risk measures typically operate on random variables, but SRMs extend this framework to sets of rando