4 papers in the archive (2025–2026), average empirical rigor 4.1/10, average math complexity 8.8/10. Quadrant mix: Holy Grail ×1, Lab Rats ×3.
Works mostly in Stochastic Control & Optimal Stopping, Insurance & Actuarial Risk, Market Microstructure, Reinforcement Learning for Trading.
Frequent co-authors: Etienne Chevalier (4), Paulin Aubert (2), Erhan Bayraktar (1), Yuqiong Wang (1), Yadh Hafsi (1), Sergio Pulido (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%).
This paper develops a model for option market making in which the hedging activity of the market maker generates price impact on the underlying asset. The option order flow is modeled by Cox processes, with intensities depending on the state of the underlying and on the market maker’s quoted prices.
We study an optimal execution strategy for purchasing a large block of shares over a fixed time horizon. The execution problem is subject to a general price impact that gradually dissipates due to market resilience. This resilience is modeled through a potentially arbitrary limit-order book shape. T
Banks must optimize risky investments, dividend payouts, and capital structure under tight Basel III solvency and liquidity constraints, while costly equity issuance serves as a distress-recovery tool. We formulate this as a stochastic control problem that reduces the high-dimensional balance-sheet
In this paper, we study an optimal dividend and capital-injection problem in a Cramér–Lundberg model where claim arrivals follow a Hawkes process, capturing clustering effects often observed in insurance portfolios. We establish key analytical properties of the value function and characterise the o