## Markovian lifting and optimal control for integral stochastic Volterra equations with completely monotone kernels

We focus on solving the optimal control problem for integral stochastic Volterra equations in a finite dimensional setting. The noise term is driven by a pure jump Lévy noise and the control acts on the intensity of the jumps. We use recent techniques proposed by Hamaguchi [1], where a crucial requirement is that the convolution kernel should be a completely monotone function. This allows us to use Bernstein’s representation and the machinery of Laplace transform to obtain a Markovian lift. It is natural that the Markovian lift, in whatever form constructed, transforms the state equation into a stochastic differential equation in an infinite-dimensional space. This space should be large enough to contain all the information about the history of the process. Hence, although the original equation is taken in a finite dimensional space, the resulting lift is always infinite dimensional. We solve the problem by using the forward-backward approach in the infinite-dimensional setting and prove the existence of the optimal control for the original problem. Under additional assumptions on the coefficients, we see that a control in closed-loop form can be achieved. [1] Y. Hamaguchi. Markovian lifting and asymptotic log-Harnack inequality for stochastic Volterra integral equations, 2023.

Area: CS23 - Stochastic processes and their applications (Katia Colaneri)

Keywords: Volterra equations, stochastic optimal control

*Il paper è coperto da copyright.*