3 papers in the archive (2023–2025), average empirical rigor 6.3/10, average math complexity 7.3/10. Quadrant mix: Holy Grail ×3.
Works mostly in Volatility Modeling & Forecasting, Machine Learning for Market Forecasting, Options & Derivatives Pricing.
Frequent co-authors: Nicola F. Zaugg (2), Shuaiqiang Liu (1), Graziana Colonna (1), Cornelis W. Oosterlee (1), Leonardo Perotti (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%).
It is a market practice to express market-implied volatilities in some parametric form. The most popular parametrizations are based on or inspired by an underlying stochastic model, like the Heston model (SVI method) or the SABR model (SABR parametrization). Their popularity is often driven by a clo
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
The lifted Heston model is a stochastic volatility model emerging as a Markovian lift of the rough Heston model and the class of rough volatility processes. The model encodes the path dependency of volatility on a set of N square-root state processes driven by a common stochastic factor. While the s