3 papers in the archive (2022–2025), average empirical rigor 3.7/10, average math complexity 8.8/10. Quadrant mix: Lab Rats ×3.
Works mostly in Volatility Modeling & Forecasting, Options & Derivatives Pricing.
Frequent co-authors: Thomas Wagenhofer (2), William Salkeld (1), Peter Bank (1), Christian Bayer (1), Luca Pelizzari (1), Benjamin Jourdain (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%).
Local stochastic volatility refers to a popular model class in applied mathematical finance that allows for “calibration-on-the-fly”, typically via a particle method, derived from a formal McKean-Vlasov equation. Well-posedness of this limit is a well-known problem in the field; the general case is
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 consider a class of stochastic processes with rough stochastic volatility, examples of which include the rough Bergomi and rough Stein-Stein model, that have gained considerable importance in quantitative finance. A basic question for such (non-Markovian) models concerns efficient numerical schem