The least squares method for option pricing revisited

TL;DR

This paper demonstrates that the least squares method for option pricing converges under broad conditions, allowing flexible implementations and suggesting that simple nonlinear extensions can be practically effective.

Contribution

It revisits the least squares method, proving convergence under general assumptions and highlighting its adaptability and potential for practical nonlinear extensions.

Findings

Convergence of least squares option pricing under broad assumptions

Flexible implementation options with varying computational complexity

Nonlinear extensions can yield satisfactory practical results

Abstract

It is shown that the the popular least squares method of option pricing converges even under very general assumptions. This substantially increases the freedom of creating different implementations of the method, with varying levels of computational complexity and flexible approach to regression. It is also argued that in many practical applications even modest non-linear extensions of standard regression may produce satisfactory results. This claim is illustrated with examples.