A Numerical scheme to approximate the solution of the planar Skorokhod embedding problem

Abstract

We present a numerical framework to approximate the $\mu$-domain in the planar Skorokhod embedding problem (PSEP), recently appeared in \cite{gross2019}. Our approach investigates the continuity and convergence properties of the solutions with respect to the underlying distribution $\mu$. We establish that, under weak convergence of a sequence of probability measures $({\mu }_n)$ with bounded support, the corresponding sequence of ${\mu }_n$-domains converges to the domain associated with $\mu$, limit of $({\mu }_n)$. We derive explicit convergence results in the $L^1$ norm, supported by a generalization using the concept of ${\alpha }_p$-convergence. Furthermore, we provide practical implementation techniques, convergence rate estimates, and numerical simulations using various distributions. The method proves robust and adaptable, offering a concrete computational pathway for approximating $\mu$-domains in the PSEP.