3 papers in the archive (2023–2025), average empirical rigor 5.7/10, average math complexity 8.3/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Options & Derivatives Pricing, Volatility Modeling & Forecasting, Stochastic Control & Optimal Stopping.
Frequent co-authors: Christian Bayer (3), Peter Bank (1), Peter K. Friz (1), Jia-Jie Zhu (1), John Schoenmakers (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 extend the signature-based primal and dual solutions to the optimal stopping problem recently introduced in [Bayer et al.: Primal and dual optimal stopping with signatures, to appear in Finance & Stochastics 2025], by integrating deep-signature and signature-kernel learning methodologies. These a
We propose two signature-based methods to solve the optimal stopping problem - that is, to price American options - in non-Markovian frameworks. Both methods rely on a global approximation result for $L^p-$functionals on rough path-spaces, using linear functionals of robust, rough path signatures. I
In this work, we introduce a novel pricing methodology in general, possibly non-Markovian local stochastic volatility (LSV) models. We observe that by conditioning the LSV dynamics on the Brownian motion that drives the volatility, one obtains a time-inhomogeneous Markov process. Using tools from ro