The least squares method for option pricing revisited
For practitioners and researchers in quantitative finance, this work provides theoretical justification and practical guidance for more flexible implementations of a widely used pricing method.
The paper proves convergence of the least squares method for option pricing under very general assumptions, increasing implementation flexibility, and shows that modest non-linear regression extensions can yield satisfactory results in practice.
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.