Convex ordering for stochastic Volterra equations and their Euler schemes

TL;DR

This paper develops methods to compare solutions of stochastic Volterra equations using convex order, introducing conditions on coefficients and analyzing Euler schemes for convergence.

Contribution

It provides new comparison conditions for stochastic Volterra equations and extends convergence results to cases with only finite first moments.

Findings

Comparison conditions depend on Euler scheme discretization

Weaker conditions for comparison in the first Euler scheme

Extended integrability assumptions to finite first moments

Abstract

In this paper, we are interested in comparing solutions to stochastic Volterra equations for the convex order on the space of continuous $\R^d$-valued paths and for the monotonic convex order when $d=1$. Even if in general these solutions are neither semi-martingales nor Markov processes, we are able to exhibit conditions on their coefficients enabling the comparison. Our approach consists in first comparing their Euler schemes and then taking the limit as the time step vanishes. We consider two types of Euler schemes depending on the way the Volterra kernels are discretized. The conditions ensuring the comparison are slightly weaker for the first scheme than for the second one and this is the other way round for convergence. Moreover, we extend the integrability needed on the starting values in the existence and convergence results in the literature to be able to only assume finite first order moments, which is the natural framework for convex ordering.