IDEAS home Printed from https://ideas.repec.org/a/sgh/erfinj/v7y2022i1p125-142.html
   My bibliography  Save this article

Index Option Pricing via Nonparametric Regression

Author

Listed:
  • Ka Po Kung

    (National University of Singapore, Singapore)

Abstract

Investors typically use the Black-Scholes (B-S) parametric model to value financial options. However, there is extensive empirical evidence that the B-S model, assuming constant volatility of stock returns, is far from adequate to price options. This paper, using nonparametric regression, incorporates a volatility-adjusting mechanism into the B-S model and prices options on the S&P 500 Index. Specifically, the upgraded B-S model, referred to as the B-S nonparametric model, is equipped with such a mechanism whose function is to assign larger volatilities for larger log returns and smaller volatilities for smaller log returns to characterize volatility clustering, a phenomenon such that large/small log returns tend to be followed by large/small log returns. Using the B-S nonparametric models as a yardstick, our simulation results show that, across the board, the B-S parametric model considerably overprices both call and put options.

Suggested Citation

  • Ka Po Kung, 2022. "Index Option Pricing via Nonparametric Regression," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 7(1), pages 125-142.
    • Handle: RePEc:sgh:erfinj:v:7:y:2022:i:1:p:125-142
    DOI: https://doi.org/10.2478/erfin-2022-0004
    as

    Download full text from publisher

    File URL: https://www.erfin.org/journal/index.php/erfin/article/view/168/65
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.2478/erfin-2022-0004?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
    ---><---

    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:sgh:erfinj:v:7:y:2022:i:1:p:125-142. 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: Dobromił Serwa (email available below). General contact details of provider: https://edirc.repec.org/data/sgwawpl.html .

    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.