Numerical Scheme for Dynkin Games under Model Uncertainty

TL;DR

This paper presents a new numerical scheme for solving continuous-time Dynkin games under model uncertainty, utilizing Skorokhod embedding to create recombining trees, enabling convergence analysis and optimal stopping strategies.

Contribution

The paper introduces a novel numerical approach using Skorokhod embedding for Dynkin games under uncertainty, with proven convergence and practical application to game options.

Findings

Convergence rates for the proposed scheme are established.

The method effectively computes optimal stopping strategies.

Applications to game options demonstrate practical utility.

Abstract

We introduce an efficient numerical scheme for continuous time Dynkin games under model uncertainty. We use the Skorokhod embedding in order to construct recombining tree approximations. This technique allows us to determine convergence rates and to construct numerically optimal stopping strategies. We apply our method to several examples of game options.