Probabilistic approximation of fully nonlinear second-order PIDEs with convergence rates for the universal robust limit theorem

Lianzi Jiang, Mingshang Hu, Gechun Liang

Published 2025-06-23Version 1

This paper develops a probabilistic approximation scheme for a class of nonstandard, fully nonlinear second-order partial integro-differential equations (PIDEs) arising from nonlinear L\'evy processes under Peng's G-expectation framework. The PIDE involves a supremum over a set of \(\alpha\)-stable L\'evy measures, potentially with degenerate diffusion and a non-separable uncertainty set, which renders existing numerical results inapplicable. We construct a recursive, piecewise-constant approximation to the viscosity solution and derive explicit error bounds. A key application of our analysis is the quantification of convergence rates for the universal robust limit theorem under sublinear expectations, unifying Peng's robust central limit theorem, laws of large numbers, and the \(\alpha\)-stable limit theorem of Bayraktar and Munk, with explicit Berry--Esseen-type bounds.