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On the robustness of least-squares Monte Carlo (LSM) for pricing American derivatives

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  • Manuel Moreno
  • Javier R. Navas

Abstract

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 impact of different basis functions on option prices. Numerical results for American put options provide evidence that a) this approach is very robust to the choice of different alternative polynomials and b) few basis functions are required. However, these conclusions are not reached when analyzing more complex derivatives.

Suggested Citation

  • Manuel Moreno & Javier R. Navas, 2001. "On the robustness of least-squares Monte Carlo (LSM) for pricing American derivatives," Economics Working Papers 543, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:543
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    More about this item

    Keywords

    Least-Squares Monte Carlo; option pricing; American options;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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