3 papers in the archive (2025–2026), average empirical rigor 6.8/10, average math complexity 8.3/10. Quadrant mix: Holy Grail ×3.
Works mostly in Volatility Modeling & Forecasting, Factor Investing & Asset Pricing.
Frequent co-authors: Emmanuel Bacry (2), Jean-François Muzy (2), Nicolas Langrené (1), Cecilia Aubrun (1), Jean-Philippe Bouchaud (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%).
We introduce the multivariate Log S-fBM model (mLog S-fBM), extending the univariate framework proposed by Wu \textit{“et al.”} to the multidimensional setting. We define the multidimensional Stationary fractional Brownian motion (mS-fBM), characterized by marginals following S-fBM dynamics and a sp
A fast simulation framework for stochastic Volterra processes based on Random Fourier Features (RFF) approximation of the kernel is developed. After recalling the main properties of Volterra processes and reviewing existing numerical simulation methods, an accelerated scheme is introduced that relie
The Nested factor model was introduced by Chicheportiche et al. to represent non-linear correlations between stocks. Stock returns are explained by a standard factor model, but the (log)-volatilities of factors and residuals are themselves decomposed into factor modes, with a common dominant volatil