3 papers in the archive (2025–2026), average empirical rigor 7.2/10, average math complexity 8.3/10. Quadrant mix: Holy Grail ×3.
Works mostly in Options & Derivatives Pricing, Volatility Modeling & Forecasting, Risk Management & Tail Risk, Portfolio Optimization.
Frequent co-authors: Blanka Horvath (3), Yannick Limmer (2), Anastasis Kratsios (1), Raeid Saqur (1), Thorsten Schmidt (1), Magnus Wiese (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 present a statically and dynamically arbitrage-free European option market model with smooth interpolation between option prices, based on the SANOS framework, and prove that its dynamics admit a unique strong solution. In spirit, it is a stochastic model for local volatility when the latter is u
This paper addresses the challenge of model uncertainty in quantitative finance, where decisions in portfolio allocation, derivative pricing, and risk management rely on estimating stochastic models from limited data. In practice, the unavailability of the true probability measure forces reliance on
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