3 papers in the archive (2023–2024), average empirical rigor 2.7/10, average math complexity 8/10. Quadrant mix: Lab Rats ×3.
Works mostly in Risk Management & Tail Risk, Options & Derivatives Pricing.
Frequent co-authors: Alessandro Mutti (2), Roberto Fontana (1), Hélène Cossette (1), Etienne Marceau (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%).
Building on the one-to-one relationship between generalized FGM copulas and multivariate Bernoulli distributions, we prove that the class of multivariate distributions with generalized FGM copulas is a convex polytope. Therefore, we find sharp bounds in this class for many aggregate risk measures, s
Model risk measures consequences of choosing a model in a class of possible alternatives. We find analytical and simulated bounds for payoff functions on classes of plausible alternatives of a given discrete model. We measure the impact of choosing a risk-neutral measure on convex derivative pricing
The key result of this paper is to characterize all the multivariate symmetric Bernoulli distributions whose sum is minimal under convex order. In doing so, we automatically characterize extremal negative dependence among Bernoulli random vectors, since multivariate distributions with minimal convex