IDEAS home Printed from https://ideas.repec.org/a/oup/jfinec/v20y2022i5p902-941..html
   My bibliography  Save this article

Discriminating Between GARCH Models for Option Pricing by Their Ability to Compute Accurate VIX Measures
[Option Valuation with Volatility Components, Fat Tails, and Non-Monotonic Pricing Kernels]

Author

Listed:
  • Christophe Chorro
  • Rahantamialisoa H Fanirisoa

Abstract

In this article, we discuss the pricing performances of a large collection of GARCH models by questioning the global synergy between the choice of the affine/nonaffine GARCH specification, the use of competing alternatives to the Gaussian distribution, the selection of an appropriate pricing kernel, and the choice of different estimation strategies based on several sets of financial information. Furthermore, the study answers an important question in relation to the correlation between the performance of a pricing scheme and its ability to forecast VIX dynamics. VIX analysis clearly appears as a parsimonious first-stage filter to discard the worst GARCH option pricing models.

Suggested Citation

  • Christophe Chorro & Rahantamialisoa H Fanirisoa, 2022. "Discriminating Between GARCH Models for Option Pricing by Their Ability to Compute Accurate VIX Measures [Option Valuation with Volatility Components, Fat Tails, and Non-Monotonic Pricing Kernels]," Journal of Financial Econometrics, Oxford University Press, vol. 20(5), pages 902-941.
    • Handle: RePEc:oup:jfinec:v:20:y:2022:i:5:p:902-941.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/jjfinec/nbaa042
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    More about this item

    Keywords

    GARCH option pricing models; GARCH implied VIX; estimation strategies; nonmonotonic stochastic discount factors;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

    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:oup:jfinec:v:20:y:2022:i:5:p:902-941.. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sofieea.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.