# Pricing path-dependent Bermudan options using Wiener chaos expansion: an embarrassingly parallel approach

## TL;DR

This paper introduces a parallelizable algorithm for pricing complex Bermudan options using Wiener chaos expansion, overcoming non-Markovian challenges and improving computational efficiency over traditional regression methods.

## Contribution

It develops a novel policy iteration method that replaces least squares with Wiener chaos expansion, enabling parallel computation for non-Markovian Bermudan option pricing.

## Key findings

- Allows non-Markovian option pricing
- Enables embarrassingly parallel computation
- Improves efficiency over traditional methods

## Abstract

In this work, we propose a new policy iteration algorithm for pricing Bermudan options when the payoff process cannot be written as a function of a lifted Markov process. Our approach is based on a modification of the well-known Longstaff Schwartz algorithm, in which we basically replace the standard least square regression by a Wiener chaos expansion. Not only does it allow us to deal with a non Markovian setting, but it also breaks the bottleneck induced by the least square regression as the coefficients of the chaos expansion are given by scalar products on the L^2 space and can therefore be approximated by independent Monte Carlo computations. This key feature enables us to provide an embarrassingly parallel algorithm.