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What Data Should Be Used to Price Options?

Author

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  • Mikhail Chernov
  • Eric Ghysels

Abstract

In this paper we propose a generic procedure for estimating and pricing options in the context of stochastic volatility models using simultaneously the fundamental price and a set of option contracts. We appraise univariate and multivariate estimation of the model in terms of pricing and hedging performance. Our results, based on the S&P 500 index contract, show that the univariate approach only involving options by and large dominates. A by-product of this finding is that we uncover a remarkably simple volatility extraction filter based on a polynomial lag structure of implied volatilities. The bivariate approach involving both the fundamental and an option appears useful when the information from the cash market provides support via the conditional kurtosis to price options. This is the case for some long-term options. Moreover, having estimated separately the risk-neutral and objective measures allows us to appraise the typical risk-neutral representations used in the literature. Using Heston's (1993) model as example we show that the usual transformation from objective to risk neutral density is not supported by the data. Nous présentons une procédure générique pour l'estimation et l'évaluation de modèles d'options avec volatilité stochastique où le sousjacent et un ensemble de contrats d'options sont utilisés simultanément. Nos résultats démontrent qu'un modèle univarié avec seulement des données d'options domine en terme d'erreurs de prix hors-échantillon et en terme de couverture. Nous trouvons également un filtre d'extraction pour la volatilité latente qui est basé sur un polynome de retards de volatilités implicites. Ayant simultanément la probabilité de risque neutre et la probabilité objective, nous pouvons vérifier, dans le contexte du modèle de Heston, si la transformation usuelle est empiriquement plausible. Nous rejetons le changement de mesure supposé dans ce modèle.

Suggested Citation

  • Mikhail Chernov & Eric Ghysels, 1998. "What Data Should Be Used to Price Options?," CIRANO Working Papers 98s-22, CIRANO.
    • Handle: RePEc:cir:cirwor:98s-22
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-059, New York University, Leonard N. Stern School of Business-.
    2. Broadie, Mark & Detemple, Jerome & Ghysels, Eric & Torres, Olivier, 2000. "Nonparametric estimation of American options' exercise boundaries and call prices," Journal of Economic Dynamics and Control, Elsevier, vol. 24(11-12), pages 1829-1857, October.
    3. Gabriele Fiorentini & Angel León & Gonzalo Rubio, "undated". "Short-term options with stochastic volatility: Estimation and empirical performance," Studies on the Spanish Economy 02, FEDEA.
    4. Ferreira García, María Eva & Gago, Mónica & Rubio Irigoyen, Gonzalo, 1999. "A Semiparametric Estimation of Liquidity Effects on Option Pricing," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    5. Darrell Duffie & Jun Pan & Kenneth Singleton, 2000. "Transform Analysis and Asset Pricing for Affine Jump-Diffusions," Econometrica, Econometric Society, vol. 68(6), pages 1343-1376, November.

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    More about this item

    Keywords

    Derivative securities; efficient method of moments; state price densities; stochastic volatility models; filtering; Titres dérivés; méthode de moments efficaces; prix d'états; filtrage; volatilité stochastique;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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