collaborators

7 papers

q-fin.CP2026

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…

q-fin.ST2026

The Fundamental Structure of Risk: From Characteristics to Covariance

Alexandre Alouadi, Charles-Albert Lehalle

Estimating the covariance structure of financial assets typically relies on historical returns, making risk models dependent on noisy and asset-specific time series. We propose the…

math.OC2026

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…

cs.LG2026

LightSBB-M: Bridging Schrödinger and Bass for Generative Diffusion Modeling

Alexandre Alouadi, Pierre Henry-Labordère, Grégoire Loeper +3

The Schrodinger Bridge and Bass (SBB) formulation, which jointly controls drift and volatility, is an established extension of the classical Schrodinger Bridge (SB). Building on th…

cs.LG2026

SBBTS: A Unified Schrödinger-Bass Framework for Synthetic Financial Time Series

Alexandre Alouadi, Grégoire Loeper, Célian Marsala +2

We study the problem of generating synthetic time series that reproduce both marginal distributions and temporal dynamics, a central challenge in financial machine learning. Existi…

math.PR2026

A PDE Derivation of the Schrödinger--Bass Bridge

Alexandre Alouadi, Pierre Henry-Labordère, Grégoire Loeper +3

This short paper announces the main results of \cite{SBB2026}, where the Schrödinger--Bass Bridge (SBB) problem is introduced and studied in full generality. Here we provide a dir…