3 papers in the archive (2026), average empirical rigor 7/10, average math complexity 6.3/10, 1 with linked code. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in Volatility Modeling & Forecasting, Options & Derivatives Pricing, Machine Learning for Market Forecasting.
Frequent co-authors: Hans Buehler (2), Blanka Horvath (2), Anastasis Kratsios (2), Yannick Limmer (2).
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%).
Neural operators (NOs) have been introduced as deep learning-powered solutions to the computational bottleneck of solving families of problems. Once trained, an NO can rapidly output approximate solutions, avoiding the need to rerun classical solvers. However, standard NO architectures cannot guaran
We present a simple, numerically efficient but highly flexible non-parametric method to construct representations of option price surfaces which are both smooth and strictly arbitrage-free across time and strike. The method can be viewed as a smooth generalization of the widely-known linear interpol
We present fast-vollib, an open-source Python library that provides high-performance European option pricing, implied volatility (IV) computation, and Greeks under the Black-76, Black-Scholes, and Black-Scholes-Merton models. The library is designed as a drop-in alternative to the de-facto-standard