IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v20y2020i1p49-67.html
   My bibliography  Save this article

Quasi-Monte Carlo-based conditional pathwise method for option Greeks

Author

Listed:
  • Chaojun Zhang
  • Xiaoqun Wang

Abstract

The calculation of option Greeks is important in financial risk management. However, the traditional pathwise method is not applicable to options with discontinuous payoffs. In this paper, using the idea of the conditional quasi-Monte Carlo method to smooth the payoff functions, we generalize the traditional pathwise method to calculate the first- and high-order Greeks. By taking a conditional expectation, the discontinuous integrand is smoothed. More importantly, the interchange of expectation and differentiation is proved to be possible. We show that the calculation of conditional expectations and then taking the derivatives with respect to the parameter of interest analytically is feasible for many common options. The new estimates for Greeks have good smoothness. For Asian and binary Asian options, for instance, our estimates are infinitely differentiable. So using the quasi-Monte Carlo method to estimate expectations improves the efficiency significantly. We also study the relationship of our method with several others in the literature, and show that our method is an extension of these methods. Numerical experiments are performed to demonstrate the high efficiency of the proposed method.

Suggested Citation

  • Chaojun Zhang & Xiaoqun Wang, 2020. "Quasi-Monte Carlo-based conditional pathwise method for option Greeks," Quantitative Finance, Taylor & Francis Journals, vol. 20(1), pages 49-67, January.
  • Handle: RePEc:taf:quantf:v:20:y:2020:i:1:p:49-67
    DOI: 10.1080/14697688.2019.1600714
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2019.1600714
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/14697688.2019.1600714?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. He, Zhijian, 2022. "Sensitivity estimation of conditional value at risk using randomized quasi-Monte Carlo," European Journal of Operational Research, Elsevier, vol. 298(1), pages 229-242.
    2. Chao Yu & Xiaoqun Wang, 2023. "Quasi-Monte Carlo-Based Conditional Malliavin Method for Continuous-Time Asian Option Greeks," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 325-360, June.

    More about this item

    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:taf:quantf:v:20:y:2020:i:1:p:49-67. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .

    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.