An Efficient Implementation of a Least Squares Monte Carlo Method for Valuing American-Style Options
Abstract
This paper presents an efficient implementation of the Least Squares Monte Carlo (LSMC) method for pricing American-style options. The approach builds on the Longstaff-Schwartz algorithm and focuses on improving computational efficiency and reducing memory requirements, particularly in high-dimensional settings.
The study formulates the pricing problem within a dynamic programming framework and introduces optimized regression techniques for estimating continuation values. Special attention is given to basis function selection, numerical stability, and simulation efficiency.
Empirical results demonstrate that the proposed implementation significantly improves performance while maintaining accuracy, even for multi-asset and high-dimensional option pricing problems. The findings confirm the suitability of the LSMC method for complex financial derivatives in practical applications.
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