3 papers in the archive (2023–2026), average empirical rigor 7.3/10, average math complexity 8.2/10. Quadrant mix: Holy Grail ×3.
Works mostly in Volatility Modeling & Forecasting, Market Microstructure, Machine Learning for Market Forecasting, High-Frequency Trading & Optimal Execution.
Frequent co-authors: Emmanuel Bacry (3), Othmane Zarhali (2), Ruihua Ruan (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
The bid-ask spread, which is defined by the difference between the best selling price and the best buying price in a Limit Order Book at a given time, is a crucial factor in the analysis of financial securities. In this study, we propose a “State-dependent Spread Hawkes model” (SDSH) that accounts f
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