IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-642-04107-5_28.html
   My bibliography  Save this book chapter

Correcting the Bias in Monte Carlo Estimators of American-style Option Values

In: Monte Carlo and Quasi-Monte Carlo Methods 2008

Author

Listed:
  • K. H. Felix Kan

    (The University of Western Ontario, Department of Applied Mathematics)

  • R. Mark Reesor

  • Tyson Whitehead

  • Matt Davison

Abstract

Existing Monte Carlo estimators of American option values are consistent but biased. This article presents a general bias reduction technique which corrects the bias due to making suboptimal exercise decisions. The derived asymptotic expression for the bias is independent of dimensionality, holds for very general underlying processes and option payoffs, and is easily evaluated. The bias is subtracted from the estimators at each exercise opportunity in order to produce bias-corrected estimators. We illustrate how to apply this technique to three methods of generating estimators — stochastic tree, stochastic mesh and least-squares Monte Carlo. Numerical results demonstrate that for a fixed sample size this technique significantly reduces the relative error for both high- and low-biased estimators.

Suggested Citation

  • K. H. Felix Kan & R. Mark Reesor & Tyson Whitehead & Matt Davison, 2009. "Correcting the Bias in Monte Carlo Estimators of American-style Option Values," Springer Books, in: Pierre L' Ecuyer & Art B. Owen (ed.), Monte Carlo and Quasi-Monte Carlo Methods 2008, pages 439-454, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-04107-5_28
    DOI: 10.1007/978-3-642-04107-5_28
    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:978-3-642-04107-5_28. 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.