3 papers in the archive (2023–2025), average empirical rigor 5.7/10, average math complexity 7.2/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Machine Learning for Market Forecasting, Options & Derivatives Pricing, Volatility Modeling & Forecasting.
Frequent co-authors: Graziana Colonna (1), Lech A. Grzelak (1), Cornelis W. Oosterlee (1), Jing Wang (1), Cornelis Vuik (1), Liexin Cheng (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%).
This paper presents a deep generative modeling framework for controllably synthesizing implied volatility surfaces (IVSs) using a variational autoencoder (VAE). Unlike conventional data-driven models, our approach provides explicit control over meaningful shape features (e.g., volatility level, slop
Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Although the method is flexible and easy to implement, it may be slow to converge. Moreover, an inaccurate solution will result when using large time steps. The Seven League scheme, a deep learning-based nu
A critical factor in adopting machine learning for time-sensitive financial tasks is computational speed, including model training and inference. This paper demonstrates that a broad class of such problems, especially those previously addressed using deep neural networks, can be efficiently solved u