3 papers in the archive (2026), average empirical rigor 5.3/10, average math complexity 7.2/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Volatility Modeling & Forecasting, Options & Derivatives Pricing, Reinforcement Learning for Trading, Fixed Income & Interest Rates.
Frequent co-authors: Andrea Pallavicini (2), Federico Cacciamani (1), Marco Pinciroli (1), Michele Trapletti (1), Edoardo Vittori (1), Héctor Folgar-Cameán (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 develop a general rough volatility model for commodities that provides an automatic calibration of the initial term structure of the futures prices and an appropriate treatment of the Samuelson effect. After the theoretical analysis of this general model, we focus on the rBergomi a
In recent decades, companies have frequently adopted share repurchase programs to return capital to shareholders or for other strategic purposes, instructing investment banks to rapidly buy back shares on their behalf. When the executing institution is allowed to hedge its exposure, it encounters se
In finance, sequential decision problems are often faced, for which reinforcement learning (RL) emerges as a promising tool for optimisation without the need of analytical tractability. However, the objective of classical RL is the expected cumulated reward, while financial applications typically re