IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-10193-9_1.html
   My bibliography  Save this book chapter

Monte Carlo Methods for Pricing American Options

In: Advances in Modeling and Simulation

Author

Listed:
  • Raul Chavez Aquino

    (Université de Montréal, Department of Economics)

  • Fabian Bastin

    (Université de Montréal, and CIRRELT, Department of Computer Science and Operations Research)

  • Maria Benazzouz

    (Université de Montréal, Department of Economics
    Desjardins)

  • Mohamed Kharrat

    (Jouf University, Department of Mathematics
    Sfax University, Laboratory of Probability and Statistics LR18ES28)

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.

Suggested Citation

  • Raul Chavez Aquino & Fabian Bastin & Maria Benazzouz & Mohamed Kharrat, 2022. "Monte Carlo Methods for Pricing American Options," Springer Books, in: Zdravko Botev & Alexander Keller & Christiane Lemieux & Bruno Tuffin (ed.), Advances in Modeling and Simulation, pages 1-20, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-10193-9_1
    DOI: 10.1007/978-3-031-10193-9_1
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-031-10193-9_1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.