4 papers in the archive (2024–2026), average empirical rigor 7.5/10, average math complexity 7.1/10. Quadrant mix: Holy Grail ×4.
Works mostly in Options & Derivatives Pricing, Risk Management & Tail Risk, Reinforcement Learning for Trading, Volatility Modeling & Forecasting.
Frequent co-authors: Zaniar Ahmadi (1), Parisa Davar (1), Jose Garrido (1), Pascal François (1), Geneviève Gauthier (1), Carlos O. Pérez-Mendoza (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 deep hedging in the context of dynamics risk measures, where sequential decisions are time-consistent. Whereas the literature in such context mainly considers low-dimensional problems with simple environment dynamics, we tackle the high-dimensional problem of basket option hedging; we show
We propose a deep hedging framework for index option portfolios, grounded in a realistic market simulator that captures the joint dynamics of S&P 500 returns and the full implied volatility surface. Our approach integrates surface-informed decisions with multiple hedging instruments and explicitly a
This paper investigates the deep hedging framework, based on reinforcement learning (RL), for the dynamic hedging of swaptions, contrasting its performance with traditional sensitivity-based rho-hedging. We design agents under three distinct objective functions (mean squared error, downside risk, an
This paper tackles the problem of mitigating catastrophic risk (which is risk with very low frequency but very high severity) in the context of a sequential decision making process. This problem is particularly challenging due to the scarcity of observations in the far tail of the distribution of cu