Finite Difference Solution Ansatz approach in Least-Squares Monte Carlo

Jiawei Huo

Papers from arXiv.org

Abstract: This article presents a simple but effective and efficient approach to improve the accuracy and stability of Least-Squares Monte Carlo for American-style option pricing as well as expected exposure calculation in valuation adjustments. The key idea is to construct the ansatz of conditional expected continuation payoff using the finite difference solution from one dimension, to be used in linear regression. This approach bridges between solving backward partial differential equations and Monte Carlo simulation, aiming at achieving the best of both worlds. Independent of model settings, the ansatz is proved to serve as a control variate to reduce the least-squares errors. We illustrate the technique with realistic examples including Bermudan options, worst of issuer callable notes and expected positive exposure on European options under valuation adjustments. The method can be considered as a generic numerical scheme across various asset classes, in particular, as an accurate method for pricing and risk-managing American-style derivatives under arbitrary dimensions.

Date: 2023-05, Revised 2025-02
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