3 papers in the archive (2025–2026), average empirical rigor 6.3/10, average math complexity 8.5/10. Quadrant mix: Holy Grail ×3.
Works mostly in High-Frequency Trading & Optimal Execution, Volatility Modeling & Forecasting, Machine Learning for Market Forecasting, Reinforcement Learning for Trading.
Frequent co-authors: Karl Sawaya (1), Thomas Valade (1), Louis Perot (1), Jiang Pu (1), Thomas Verrier (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 consider the problem of optimal liquidation with linear market impact, as in the seminal work of Almgren and Chriss, but with an asset driven by a fractional Brownian motion. For $H \neq 1/2$, we observe that the deterministic solution given by Almgren and Chriss is no longer optimal. We develop
Relative entropy, as a divergence metric between two distributions, can be used for offline change-point detection and extends classical methods that mainly rely on moment-based discrepancies. To build a statistical test suitable for this context, we study the distribution of empirical relative entr
The linear fractional stable motion (LFSM) extends the fractional Brownian motion (fBm) by considering $α$-stable increments. We propose a method to forecast future increments of the LFSM from past discrete-time observations, using the conditional expectation when $α>1$ or a semimetric projection ot