Recombining tree approximations for Game Options in Local Volatility models

TL;DR

This paper presents a numerical method using Skorokhod embedding to create recombining tree approximations for diffusions with general coefficients, enabling efficient optimal stopping and game option valuation.

Contribution

It introduces a novel approach combining Skorokhod embedding with recombining trees for diffusions, providing convergence rates and nearly optimal stopping times.

Findings

Effective scheme for game options in local volatility models

Demonstrated convergence and efficiency through examples

Constructed nearly optimal stopping times

Abstract

In this paper we introduce a numerical method for optimal stopping in the framework of one dimensional diffusion. We use the Skorokhod embedding in order to construct recombining tree approximations for diffusions with general coefficients. This technique allows us to determine convergence rates and construct nearly optimal stopping times which are optimal at the same rate. Finally, we demonstrate the efficiency of our scheme with several examples of game options.