3 papers in the archive (2023–2026), average empirical rigor 6.3/10, average math complexity 8.3/10, 1 with linked code. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Crypto Markets & DeFi, Stochastic Control & Optimal Stopping, Volatility Modeling & Forecasting, Market Microstructure.
Frequent co-authors: Sebastian Jaimungal (2), Diogo Duarte (1), Lucas X. Ferreira (1), Emmanuel Gobet (1), Felipe J. P. Antunes (1), Max O. Souza (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 develop a no-arbitrage framework to recover implied fee rates from option-implied information and quantify fee compensation for liquidity providers in automated market maker (AMM) pools. Expected accumulated fees equal the current pool value minus the risk-neutral value of its terminal payoff, re
We present a novel numerical method for solving McKean-Vlasov forward-backward stochastic differential equations (MV-FBSDEs) with common noise, combining Picard iterations, elicitability and deep learning. The key innovation involves elicitability to derive a path-wise loss function, enabling effici
This article explores the optimisation of trading strategies in Constant Function Market Makers (CFMMs) and centralised exchanges. We develop a model that accounts for the interaction between these two markets, estimating the conditional dependence between variables using the concept of conditional