3 papers in the archive (2022–2026), average empirical rigor 3.7/10, average math complexity 8.5/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Risk Management & Tail Risk, Portfolio Optimization, Stochastic Control & Optimal Stopping, Insurance & Actuarial Risk.
Frequent co-authors: Shihao Zhu (2), Emanuele Borgonovo (1), Massimo Marinacci (1), Nicole Bäuerle (1), Christian Laudagé (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 study optimal collective investment subject to a Value-at-Risk (VaR)-type constraint that accounts for uncertainty about future regulatory requirements. The constraint incorporates several possible wealth thresholds and imposes a minimum weighted average probability of meeting them. We characteri
We study continuous-time portfolio choice with nonlinear payoffs under smooth ambiguity and Bayesian learning. We develop a general framework for dynamic, non-concave asset allocation that accommodates nonlinear payoffs, broad utility classes, and flexible ambiguity attitudes. Dynamic consistency is
This paper extends the utility maximization literature by combining partial information and (robust) regulatory constraints. Partial information is characterized by the fact that the stock price itself is observable by the optimizing financial institution, but the outcome of the market price of the