3 papers in the archive (2025–2026), average empirical rigor 7.4/10, average math complexity 8.7/10. Quadrant mix: Holy Grail ×3.
Works mostly in Volatility Modeling & Forecasting, Market Simulation & Agent-Based Models, Market Microstructure, Machine Learning for Market Forecasting.
Frequent co-authors: Guillaume Maitrier (1), Jean-Philippe Bouchaud (1), Alexandre Alouadi (1), Célian Marsala (1), Othmane Mazhar (1), Huyên Pham (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 work extends and complements our previous theoretical paper on the subtle interplay between impact, order flow and volatility. In the present paper, we generate synthetic market data following the specification of that paper and show that the approximations made there are actually justified, wh
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 study the problem of generating synthetic time series that reproduce both marginal distributions and temporal dynamics, a central challenge in financial machine learning. Existing approaches typically fail to jointly model drift and stochastic volatility, as diffusion-based methods fix the volati