3 papers in the archive (2023–2025), average empirical rigor 3.2/10, average math complexity 8.7/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Options & Derivatives Pricing, Volatility Modeling & Forecasting, Stochastic Control & Optimal Stopping, Machine Learning for Market Forecasting.
Frequent co-authors: Severin Nilsson (1), Johannes Wiesel (1), Jan Obłój (1), Nicolas Langrené (1), Jiahao Wu (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%).
In this paper, we introduce two novel methods to solve the American-style option pricing problem and its dual form at the same time using neural networks. Without applying nested Monte Carlo, the first method uses a series of neural networks to simultaneously compute both the lower and upper bounds
We consider the robust pricing and hedging of American options in a continuous time setting. We assume asset prices are continuous semimartingales, but we allow for general model uncertainty specification via adapted closed convex constraints on the volatility. We prove the robust pricing-hedging du
We extend the Benamou-Brenier formula from classical optimal transport to weak optimal transport and show that the barycentric optimal transport problem studied by Gozlan and Juillet has a dynamic analogue. We also investigate a martingale relaxation of this problem, and relate it to the martingale