  We consider the problem of simultaneously approximating the conditional
distribution of market prices and their log returns with a single machine
learning model. We show that an instance of the GDN model of Kratsios and Papon
(2022) solves this problem without having prior assumptions on the market's
"clipped" log returns, other than that they follow a generalized
Ornstein-Uhlenbeck process with a priori unknown dynamics. We provide universal
approximation guarantees for these conditional distributions and contingent
claims with a Lipschitz payoff function.
