6 papers in the archive (2022–2026), average empirical rigor 2/10, average math complexity 8.4/10. Quadrant mix: Lab Rats ×6.
Works mostly in Risk Management & Tail Risk, Options & Derivatives Pricing, Factor Investing & Asset Pricing, Machine Learning for Market Forecasting.
Frequent co-authors: Marco Frittelli (4), Marco Maggis (3), Emanuela Rosazza Gianin (1), Edoardo Berton (1), Matteo Burzoni (1), Enea Monzio Compagnoni (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 consider the problem of optimally sharing a financial position among agents with potentially different reference risk measures. The problem is equivalent to computing the infimal convolution of the risk metrics and finding the so-called optimal allocations. We propose a neural network-based frame
Shortfall systemic (multivariate) risk measures $ρ$ defined through an $N$-dimensional multivariate utility function $U$ and random allocations can be represented as classical (one dimensional) shortfall risk measures associated to an explicitly determined $1$-dimensional function constructed from $
We introduce the notions of Collective Arbitrage and of Collective Super-replication in a discrete-time setting where agents are investing in their markets and are allowed to cooperate through exchanges. We accordingly establish versions of the fundamental theorem of asset pricing and of the pricing
Within a general semimartingale framework, we study the relationship between collective market efficiency and individual rationality. We derive a necessary and sufficient condition for the existence of (possibly zero-sum) exchanges among agents that strictly increase their indirect utilities and cha
This paper builds on “Collective Arbitrage and the Value of Cooperation” by Biagini et al. (2025, forthcoming in “Finance and Stochastics”), which introduced in discrete time the notions of collective arbitrage and super-replication in a multi-agent market framework, where agents may operate in seve
We consider a family of conditional nonlinear expectations defined on the space of bounded random variables and indexed by the class of all the sub-sigma-algebras of a given underlying sigma-algebra. We show that if this family satisfies a natural consistency property, then it collapses to a conditi