3 papers in the archive (2023–2026), average empirical rigor 5.2/10, average math complexity 8/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Options & Derivatives Pricing, Machine Learning for Market Forecasting, Volatility Modeling & Forecasting, Fixed Income & Interest Rates.
Frequent co-authors: Álvaro Leitao (2), Fernando Alonso (1), Alberto Manzano (1), Gonzalo Ferro (1), Andrés Gómez (1), Roberto Daluiso (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 this work we present an alternative methodology to the standard Quantum Accelerated Monte Carlo (QAMC) applied to derivatives pricing. Our pipeline benefits from the combination of a new encoding protocol, referred to as the direct encoding, and a amplitude estimation algorithm, the modified Real
The ongoing progress in quantum technologies has fueled a sustained exploration of their potential applications across various domains. One particularly promising field is quantitative finance, where a central challenge is the pricing of financial derivatives-traditionally addressed through Monte Ca