We prove new error estimates for the Longstaff–Schwartz algorithm. We establish an <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$O(\log^{\frac{1}{2}}(N)N^{-\frac{1}{2}})$</EquationSource> </InlineEquation> convergence rate for the expected L <Superscript>2</Superscript> sample error of this algorithm (where N is the number of Monte Carlo sample paths), whenever the approximation architecture of...</superscript></equationsource></inlineequation>

This paper proposes an innovative algorithm that significantly improves on the approximation of the optimal early exercise boundary obtained with simulation based methods for American option pricing. The method works by exploiting and leveraging the information in multiple cross-sectional...

This paper proposes an innovative algorithm that significantly improves on the approximation of the optimal early exercise boundary obtained with simulation based methods for American option pricing. The method works by exploiting and leveraging the information in multiple cross-sectional...

This paper analyses the robustness of Least-Squares Monte Carlo, a technique recently proposed by Longstaff and Schwartz (2001) for pricing American options. This method is based on least-squares regressions in which the explanatory variables are certain polynomial functions. We analyze the...

This paper analyses the robustness of Least-Squares Monte Carlo, a technique proposed by Longstaff and Schwartz (2001) for pricing American options. This method is based on least-squares regressions in which the explanatory variables are certain polynomial functions. We analyze the impact of...