7 papers
Deep-MKV-TS: Path-Dependent McKean--Vlasov Control for Financial Time Series Generation
Samer El Boustany, Théo Basseras, Samy Mekkaoui +3
We introduce Deep-MKV-TS, a path-dependent McKean-Vlasov framework for financial scenario generation. The stochastic dynamics are chosen by matching selected path and volatility fe…
Policy Gradient Learning for Distributionally Robust Markov Decision Processes under Wasserstein Ambiguity
Yadh Hafsi, Samy Mekkaoui, Huyên Pham +1
We study finite-horizon Markov decision processes under distributional uncertainty in the transition kernels and develop a policy-gradient framework for Wasserstein distributionall…
Learning Generative Dynamics with Soft Law Constraints: A McKean-Vlasov FBSDE Approach
Samer El Boustany, Samy Mekkaoui, Yadh Hafsi +2
We propose a generative framework for learning stochastic dynamics from endpoint and intermediate distributional observations. The method formulates generation as a McKean-Vlasov c…
Optimal Execution under Liquidity Uncertainty
Etienne Chevalier, Yadh Hafsi, Vathana Ly Vath +1
We study an optimal execution strategy for purchasing a large block of shares over a fixed time horizon. The execution problem is subject to a general price impact that gradually d…
Trading in CEXs and DEXs with Priority Fees and Stochastic Delays
Philippe Bergault, Yadh Hafsi, Leandro Sánchez-Betancourt
We develop a mixed control framework that combines absolutely continuous controls with impulse interventions subject to stochastic execution delays. The model extends current impul…
Reinforcement Learning in Queue-Reactive Models: Application to Optimal Execution
Tomas Espana, Yadh Hafsi, Fabrizio Lillo +1
We investigate the use of Reinforcement Learning for the optimal execution of meta-orders, where the objective is to execute incrementally large orders while minimizing implementat…