4 papers in the archive (2022–2026), average empirical rigor 5.4/10, average math complexity 8.2/10. Quadrant mix: Holy Grail ×2, Lab Rats ×2.
Works mostly in Stochastic Control & Optimal Stopping, Risk Management & Tail Risk, Commodities & Energy Markets, Machine Learning for Market Forecasting.
Frequent co-authors: Mohan Yang (1), Géraldine Bouveret (1), Smail Ibbou (1), Antoine Jacquier (1), Lionel Sopgoui (1), Junchao Chen (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%).
The multidimensional Uncertain Volatility Model leads to robust option pricing problems under joint volatility and correlation uncertainty. Their numerical resolution quickly becomes challenging because the associated stochastic control problem is high-dimensional. We propose a backward actor-critic
We propose a new probabilistic scheme which combines deep learning techniques with high order schemes for backward stochastic differential equations belonging to the class of Runge-Kutta methods to solve high-dimensional semi-linear parabolic partial differential equations. Our approach notably exte
We study how the climate transition through a low-carbon economy, implemented by carbon pricing, propagates in a credit portfolio and precisely describe how carbon price dynamics affects credit risk measures such as probability of default, expected and unexpected losses. We adapt a stochastic multis
We study an implementation of the theoretical splitting scheme introduced in [Chassagneux and Yang, 2022] for singular FBSDEs [Carmona and Delarue 2013] and their associated quasi-linear degenerate PDEs. The fully implementable algorithm is based on particles approximation of the transport operator