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Recovering Volatility from Option Prices by Evolutionary Optimization

Author

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  • Sana Ben Hamida

    (CMAP - Centre de Mathématiques Appliquées de l'Ecole polytechnique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique, ESTI - Ecole Supérieure de Technologie et d'Informatique [Tunis-Carthage] - UCAR - Université de Carthage (Tunisie))

  • Rama Cont

    (CMAP - Centre de Mathématiques Appliquées de l'Ecole polytechnique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique)

Abstract

We propose a probabilistic approach for estimating parameters of an option pricing model from a set of observed option prices. Our approach is based on a stochastic optimization algorithm which generates a random sample from the set of global minima of the in-sample pricing error and allows for the existence of multiple global minima. Starting from an IID population of candidate solutions drawn from a prior distribution of the set of model parameters, the population of parameters is updated through cycles of independent random moves followed by "selection" according to pricing performance. We examine conditions under which such an evolving population converges to a sample of calibrated models. The heterogeneity of the obtained sample can then be used to quantify the degree of ill-posedness of the inverse problem: it provides a natural example of a coherent measure of risk, which is compatible with observed prices of benchmark ("vanilla") options and takes into account the model uncertainty resulting from incomplete identification of the model. We describe in detail the algorithm in the case of a diffusion model, where one aims at retrieving the unknown local volatility surface from a finite set of option prices, and illustrate its performance on simulated and empirical data sets of index options.

Suggested Citation

  • Sana Ben Hamida & Rama Cont, 2005. "Recovering Volatility from Option Prices by Evolutionary Optimization," Post-Print hal-02490586, HAL.
    • Handle: RePEc:hal:journl:hal-02490586
    DOI: 10.2139/ssrn.546882
    Note: View the original document on HAL open archive server: https://hal.parisnanterre.fr/hal-02490586
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    References listed on IDEAS

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    1. Damiano Brigo & Fabio Mercurio, 2002. "Lognormal-Mixture Dynamics And Calibration To Market Volatility Smiles," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 427-446.
    2. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    3. Marco Avellaneda & Craig Friedman & Richard Holmes & Dominick Samperi, 1997. "Calibrating volatility surfaces via relative-entropy minimization," Applied Mathematical Finance, Taylor & Francis Journals, vol. 4(1), pages 37-64.
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    Cited by:

    1. Cho-Hoi Hui & Tsz-Kin Chung, 2010. "The Risk of Sudden Depreciation of the Euro in the Sovereign Debt Crisis of 2009-2010," Working Papers 252010, Hong Kong Institute for Monetary Research.
    2. Manfred Gilli & Enrico Schumann, 2012. "Heuristic optimisation in financial modelling," Annals of Operations Research, Springer, vol. 193(1), pages 129-158, March.
    3. Martin Tegn'er & Stephen Roberts, 2019. "A Probabilistic Approach to Nonparametric Local Volatility," Papers 1901.06021, arXiv.org, revised Jan 2019.
    4. A. Monteiro & R. Tütüncü & L. Vicente, 2011. "Estimation of risk-neutral density surfaces," Computational Management Science, Springer, vol. 8(4), pages 387-414, November.
    5. Mascagni Michael & Qiu Yue & Hin Lin-Yee, 2014. "High performance computing in quantitative finance: A review from the pseudo-random number generator perspective," Monte Carlo Methods and Applications, De Gruyter, vol. 20(2), pages 101-120, June.
    6. Mrázek, Milan & Pospíšil, Jan & Sobotka, Tomáš, 2016. "On calibration of stochastic and fractional stochastic volatility models," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1036-1046.
    7. Cristian Homescu, 2011. "Implied Volatility Surface: Construction Methodologies and Characteristics," Papers 1107.1834, arXiv.org.
    8. Marc Chataigner & Stéphane Crépey, 2019. "Credit Valuation Adjustment Compression by Genetic Optimization," Risks, MDPI, vol. 7(4), pages 1-21, September.
    9. Fig-Talamanca, Gianna, 2009. "Testing volatility autocorrelation in the constant elasticity of variance stochastic volatility model," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2201-2218, April.
    10. Christa Cuchiero & Guido Gazzani & Sara Svaluto-Ferro, 2022. "Signature-based models: theory and calibration," Papers 2207.13136, arXiv.org.
    11. Martin Tegner & Stephen Roberts, 2021. "A Bayesian take on option pricing with Gaussian processes," Papers 2112.03718, arXiv.org.
    12. Jimin Lin & Guixin Liu, 2024. "Neural Term Structure of Additive Process for Option Pricing," Papers 2408.01642, arXiv.org, revised Oct 2024.
    13. Charles Guy Njike Leunga & Donatien Hainaut, 2024. "Affine Heston model style with self-exciting jumps and long memory," Annals of Finance, Springer, vol. 20(1), pages 1-43, March.
    14. Volk-Makarewicz, Warren & Borovkova, Svetlana & Heidergott, Bernd, 2022. "Assessing the impact of jumps in an option pricing model: A gradient estimation approach," European Journal of Operational Research, Elsevier, vol. 298(2), pages 740-751.
    15. Guido Gazzani & Julien Guyon, 2024. "Pricing and calibration in the 4-factor path-dependent volatility model," Papers 2406.02319, arXiv.org.

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