4 papers in the archive (2023–2025), average empirical rigor 5/10, average math complexity 8.4/10. Quadrant mix: Holy Grail ×2, Lab Rats ×2.
Works mostly in Options & Derivatives Pricing, Volatility Modeling & Forecasting, Stochastic Control & Optimal Stopping, Factor Investing & Asset Pricing.
Frequent co-authors: Luca Pelizzari (3), Eduardo Abi Jaber (1), Simon Breneis (1), Peter Bank (1), Peter K. Friz (1), Jia-Jie Zhu (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
We show that the state spaces of multifactor Markovian processes, coming from approximations of nonnegative Volterra processes, are given by explicit linear transformation of the nonnegative orthant. We demonstrate the usefulness of this result for applications, including simulation schemes and PDE