Spike Variations for Stochastic Volterra Integral Equations

TL;DR

This paper extends the spike variation technique to stochastic Volterra integral equations, overcoming quadratic term challenges to establish a Pontryagin maximum principle for these equations.

Contribution

It introduces a novel approach using an auxiliary process and Itô's formula to derive a maximum principle for stochastic Volterra integral equations.

Findings

Established a Pontryagin maximum principle for FSVIEs.

Developed a new method to handle quadratic terms in stochastic Volterra equations.

Extended the spike variation technique to a new class of stochastic integral equations.

Abstract

Spike variation technique plays a crucial role in deriving Pontryagin's type maximum principle of optimal controls for differential equations of several types, including ordinary differential equations (ODEs), partial differential equations (PDEs), and stochastic differentia equations (SDEs), when the control domains are not assumed to be convex. This technique also applies to (deterministic forward) Volterra intrgral equations (FVIEs). It is natural to expect that such a technique could be extended to the case of (forward) stochastic Volterra integral equations (FSVIEs). However, by mimicking the case of SDEs, one encounters an essential difficulty of handling an involved quadratic term. To overcome the difficulty, we introduce an auxiliary process for which one can use It\^o's formula, and adopt a trick used in linear-quadratic stochastic optimal control problems. Then a suitable representation of the above-mentioned quadratic form is obtained, and the second order adjoint equations are derived. Consequently, the maximum principle of Pontryagin type is established. Some relevant extensions are investigated as well.