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Discriminating between GARCH models for option pricing by their ability to compute accurate VIX measures
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Discriminating between GARCH models for option pricing by their ability to compute accurate VIX measures

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Abstract

In this paper, we discuss the pricing performances of a large collection of GARCH models by questioning the global synergy between the choice of the affine/non-affine GARCH specification, the use of competing alternatives to the Gaussian distribution, the selection of an appropriate pricing kernel and 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 & R.H. Fanirisoa Zazaravaka, 2019. "Discriminating between GARCH models for option pricing by their ability to compute accurate VIX measures," Documents de travail du Centre d'Economie de la Sorbonne 19014, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    • Handle: RePEc:mse:cesdoc:19014
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    More about this item

    Keywords

    GARCH option pricing models; GARCH implied VIX; estimation strategies; non-monotonic 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

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