On the Robustness of Least-Squares Monte Carlo (LSM) for Pricing American Derivatives

By Manuel Moreno, Javier F. Navas. 2007

164 citations recorded by the source corpus.

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 different basis functions on option prices. Numerical results for American put options show that this approach is quite robust to the choice of basis functions. For more complex derivatives, this choice can slightly affect option prices.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/f3f2548b7f183aa3936f9d391a0323ff6a6d44a7

How researchers see this paper

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In Economics and Econometrics

1. Ari D. Andricopoulos
2. Martin Widdicks
3. David P. Newton
4. Peter W. Duck

In Finance

1. Ari D. Andricopoulos
2. Martin Widdicks
3. David P. Newton
4. Peter W. Duck
5. Sam D. Howison

Citations in the corpus

• Regression-based algorithms for life insurance contracts with surrender guarantees (2010, unknown): “The joint effect of H and M on convergence is less clear: some non-asymptotic results can be found in Gobet, Lemor and Warin (2005), while interesting numerical investigations are reported in Moreno and Navas (2003) and Stentoft (2004).”
• Extending quadrature methods to value multi-asset and complex path dependent options (2007, methodology): “A detailed appraisal of this technique by Moreno and Navas (2003) investigates the use of various polynomial fits and numbers of basis functions.”