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Monte Carlo Methods for Pricing American Options

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Advances in Modeling and Simulation

Abstract

American options are widespread in the financial market. We review various popular techniques used to value American options, as well as Malliavin calculus and recent approaches proposed in machine learning, and examine their performance on synthetic and real data. Our preliminary results confirm that pricing an American put option on a single asset can be efficiently done using regression approaches, and random forests are competitive in terms of accuracy and computation times. Malliavin calculus, despite its interesting mathematical properties, is not competitive for American option pricing, and neural networks are difficult to design in the context of options. Variance reduction, achieved here by means of control variates, is a crucial tool to obtain reliable results at a reasonable cost.

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Author information

Authors and Affiliations

  1. Department of Economics, Université de Montréal, Montréal, QC, Canada

    Raul Chavez Aquino & Maria Benazzouz

  2. Department of Computer Science and Operations Research, Université de Montréal, and CIRRELT, Montréal, QC, Canada

    Fabian Bastin

  3. Desjardins, Laval, QC, Canada

    Maria Benazzouz

  4. Department of Mathematics, Jouf University, Sakaka, Saudi Arabia

    Mohamed Kharrat

  5. Laboratory of Probability and Statistics LR18ES28, Sfax University, Sfax, Tunisia

    Mohamed Kharrat

Authors
  1. Raul Chavez Aquino
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  2. Fabian Bastin
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  3. Maria Benazzouz
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  4. Mohamed Kharrat
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Corresponding author

Correspondence to Fabian Bastin .

Editor information

Editors and Affiliations

  1. Randwick, NSW, Australia

    Zdravko Botev

  2. NVIDIA, Berlin, Germany

    Alexander Keller

  3. University of Waterloo, Waterloo, ON, Canada

    Christiane Lemieux

  4. Rennes Bretagne-Atlantique, INRIA, Rennes Cedex, France

    Bruno Tuffin

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Chavez Aquino, R., Bastin, F., Benazzouz, M., Kharrat, M. (2022). Monte Carlo Methods for Pricing American Options. In: Botev, Z., Keller, A., Lemieux, C., Tuffin, B. (eds) Advances in Modeling and Simulation. Springer, Cham. https://doi.org/10.1007/978-3-031-10193-9_1

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