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

Christophe Chorro () and Rahantamialisoa Fanirisoa ()
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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

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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.

Date: 2021-01-22
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Published in Journal of Financial Econometrics, 2021, pp.nbaa042. ⟨10.1093/jjfinec/nbaa042⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03131121

DOI: 10.1093/jjfinec/nbaa042

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