3 papers in the archive (2024–2026), average empirical rigor 3.3/10, average math complexity 8.7/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Machine Learning for Market Forecasting, Volatility Modeling & Forecasting, Options & Derivatives Pricing, Stochastic Control & Optimal Stopping.
Frequent co-authors: Chenguang Liu (2), Shaïn Afzali (1), Serena Della Corte (1), Jasper Rou (1), Alexandros Saplaouras (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%).
Neural network-based approaches have emerged as efficient alternatives to traditional optimization-based procedures for the calibration of stochastic volatility models. However, existing work has focused primarily on predictive accuracy, with comparatively little attention devoted to understanding t
The aim of this article is to provide a firm mathematical foundation for the application of deep gradient flow methods (DGFMs) for the solution of (high-dimensional) partial differential equations (PDEs). We decompose the generalization error of DGFMs into an approximation and a training error. We f
We consider Lévy processes that are approximated by compound Poisson processes and, correspondingly, BSDEs driven by Lévy processes that are approximated by BSDEs driven by their compound Poisson approximations. We are interested in the rate of convergence of the approximate BSDEs to the ones driven