3 papers in the archive (2025–2026), average empirical rigor 5/10, average math complexity 7.2/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Risk Management & Tail Risk, Portfolio Optimization, Machine Learning for Market Forecasting, Stochastic Control & Optimal Stopping.
Frequent co-authors: Véronique Maume-Deschamps (2), William Thevenot (2), Aurélien Alfonsi (1), Ahmed Kebaier (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 a discrete-time multi-period portfolio optimization problem under an explicit constraint on the Deviation Conditional Value-at-Risk (DCVaR), defined as the excess of Conditional Value-at-Risk over expected terminal wealth. The objective is to maximize expected return subject to a global tai
Motivated by recent results on the dual formulation of optimal stopping problems, we investigate in this short paper how the knowledge of an approximating dual martingale can improve the efficiency of primal methods. In particular, we show on numerical examples that accurate approximations of a dual
We study a continuous-time portfolio optimization problem under an explicit constraint on the Deviation Conditional Value-at-Risk (DCVaR), defined as the difference between the CVaR and the expected terminal wealth. While the mean-CVaR framework has been widely explored, its time-inconsistency compl