3 papers in the archive (2023–2025), average empirical rigor 5/10, average math complexity 8.6/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Volatility Modeling & Forecasting, Options & Derivatives Pricing.
Frequent co-authors: Òscar Burés (3), Elisa Alòs (2), Rafael de Santiago (1), Mishari Al-Foraih (1), Jan Pospíšil (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 two complementary methodologies for calibrating implied volatility surfaces: analytical approximations and data-driven models based on rough path theory. On the analytical side, we revisit a second-order asymptotic expansion for the Heston model, and we propose a new, VIX-based calibration
Using Malliavin calculus techniques, we obtain formulas for computing Greeks under different rough Volterra stochastic volatility models. Due to the fact that underlying prices are not always square integrable, we extend the classical integration by parts formula to integrable but not necessarily sq
In this paper we use Malliavin Calculus techniques in order to obtain expressions for the short-time behavior of the at-the-money implied volatility (ATM-IV) level and skew for a jump-diffusion stock price. The diffusion part is assumed to be the stochastic volatility Bachelier model and the jumps a