4 papers in the archive (2023–2025), average empirical rigor 2.2/10, average math complexity 8.8/10. Quadrant mix: Lab Rats ×4.
Works mostly in Volatility Modeling & Forecasting, Options & Derivatives Pricing.
Frequent co-authors: Kexin Shao (2), Gudmund Pammer (1), Peter K. Friz (1), Thomas Wagenhofer (1), Alexandre Zhou (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
For many examples of couples $(μ,ν)$ of probability measures on the real line in the convex order, we observe numerically that the Hobson and Neuberger martingale coupling, which maximizes for $ρ=1$ the integral of $|y-x|^ρ$ with respect to any martingale coupling between $μ$ and $ν$, is still a max
While many questions in robust finance can be posed in the martingale optimal transport framework or its weak extension, others like the subreplication price of VIX futures, the robust pricing of American options or the construction of shadow couplings necessitate additional information to be incorp
In this note, we complete the analysis of the Martingale Wasserstein Inequality started in arXiv:2011.11599 by checking that this inequality fails in dimension $d\ge 2$ when the integrability parameter $ρ$ belongs to $[1,2)$ while a stronger Maximal Martingale Wasserstein Inequality holds whatever t