3 papers in the archive (2023–2026), average empirical rigor 5.2/10, average math complexity 8.2/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Options & Derivatives Pricing, Machine Learning for Market Forecasting, Market Microstructure.
Frequent co-authors: Carlos Vázquez (2), Fernando Alonso (1), Julien Hok (1), Alberto Manzano (1), Gonzalo Ferro (1), Andrés Gómez (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 introduces an end-to-end framework for multi-asset option pricing that combines market-consistent risk-neutral density recovery with quantum-accelerated numerical integration. We first calibrate arbitrage-free marginal distributions from European option quotes using the Normal Inverse Gaus
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