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Discriminating Between GARCH Models for Option Pricing by Their Ability to Compute Accurate VIX Measures

Author

Listed:
  • Christophe Chorro

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Rahantamialisoa Fanirisoa

    (UP1 - Université Paris 1 Panthéon-Sorbonne, Università Ca’ Foscari)

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 Fanirisoa, 2021. "Discriminating Between GARCH Models for Option Pricing by Their Ability to Compute Accurate VIX Measures," Post-Print hal-03131121, HAL.
    • Handle: RePEc:hal:journl:hal-03131121
    DOI: 10.1093/jjfinec/nbaa042
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    Cited by:

    1. Wu, Xinyu & Zhao, An & Liu, Li, 2023. "Forecasting VIX using two-component realized EGARCH model," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).

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