activity
19972025
most citedConvex Analysis for LQG Systems with Applications to Major Minor LQG Mean-Field Game Systems

27 citations · 159 across the 58 of their papers we have counts for

collaborators

69 papers

q-fin.TR2025

Equilibrium Liquidity and Risk Offsetting in Decentralised Markets

Fayçal Drissi, Xuchen Wu, Sebastian Jaimungal

We study the economic viability of liquidity provision in decentralised exchanges (DEXs) within a structural framework in which market outcomes are endogenous. We formulate strateg…

cs.LG2025

Deep Learning and Elicitability for McKean-Vlasov FBSDEs With Common Noise

Felipe J. P. Antunes, Yuri F. Saporito, Sebastian Jaimungal

We present a novel numerical method for solving McKean--Vlasov forward--backward stochastic differential equations (MV--FBSDEs) with common noise, combining Picard iterations, elic…

q-fin.TR2025

Deep reinforcement learning for optimal trading with partial information

Andrea Macrì, Sebastian Jaimungal, Fabrizio Lillo

Reinforcement Learning (RL) has attracted increasing interest in financial applications, including optimal trading and execution. However, the use of RL for optimal trading strateg…

q-fin.TR2025

Deviations from Tradition: Stylized Facts in the Era of DeFi

Daniele Maria Di Nosse, Federico Gatta, Fabrizio Lillo +1

Decentralized Exchanges (DEXs) are now a significant component of the financial world where billions of dollars are traded daily. Differently from traditional markets, which are ty…

q-fin.MF2025

Multi-Agent Reinforcement Learning for Greenhouse Gas Offset Credit Markets

Liam Welsh, Udit Grover, Sebastian Jaimungal

Climate change is a major threat to the future of humanity, and its impacts are being intensified by excess man-made greenhouse gas emissions. One method governments can employ to…

q-fin.RM2025

Model Combination in Risk Sharing under Ambiguity

Emma Kroell, Sebastian Jaimungal, Silvana M. Pesenti

We consider the problem of an agent who faces losses in continuous time over a finite time horizon and may choose to share some of these losses with a counterparty. The agent is un…