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

This paper develops a probabilistic approximation scheme for a class of nonstandard, fully nonlinear second-order partial integro-differential equations (PIDEs) arising from nonlinear Lévy processes under Peng's G-expectation framework. The PIDE involves a supremum over a set of \(α\)-stable Lévy 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 \(α\)-stable limit theorem of Bayraktar and Munk, with explicit Berry--Esseen-type bounds.

Publication Details

Journal
ArXiv.org
Published
2025-06-23
Primary Topic
Stochastic processes and financial applications
Type
article
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article

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

ArXiv.org
Stochastic processes and financial applications
article

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

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Abstract

This paper develops a probabilistic approximation scheme for a class of nonstandard, fully nonlinear second-order partial integro-differential equations (PIDEs) arising from nonlinear Lévy processes under Peng's G-expectation framework. The PIDE involves a supremum over a set of \(α\)-stable Lévy 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 \(α\)-stable limit theorem of Bayraktar and Munk, with explicit Berry--Esseen-type bounds.

ArXiv.org
Reduced inequalities
Openalex Percentile: Top 42%
Stochastic processes and financial applications
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