IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-02533014.html
   My bibliography  Save this paper

A direct formulation of implied volatility in the Black-Scholes model

Author

Listed:
  • Philippe Jacquinot

    (GREGOR - Groupe de Recherche en Gestion des Organisations - UP1 - Université Paris 1 Panthéon-Sorbonne - IAE Paris - Sorbonne Business School)

  • Nikolay Sukhomlin

    (UASD - Universidad Autónoma de Santo Domingo = Autonomous University of Santo-Domingo)

Abstract

The inverse problem of option pricing, also known as market calibration, attracted the attention of a large number of practitioners and academics, from the moment that Black-Scholes formulated their model. The search for an explicit expression of volatility as a function of the observable variables has generated a vast body of literature, forming a specific branch of quantitative finance. But up to now, no exact expression of implied volatility has been obtained. The main result of this paper is such an exact expression. Firstly, a formula was deduced analytically. Secondly, it was shown that this expression is actually an exact inversion, using simulated data. Thirdly, it was shown that the methodology can be used to express implied volatility in more sophisticated models, such as the Blenman and Clark model. In the conclusion, discussion of the results was made.

Suggested Citation

  • Philippe Jacquinot & Nikolay Sukhomlin, 2010. "A direct formulation of implied volatility in the Black-Scholes model," Post-Print hal-02533014, HAL.
  • Handle: RePEc:hal:journl:hal-02533014
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    as
    1. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871, November.
    2. Kataoka, Haruo & Hashimoto, Hiroaki, 1995. "New conservation laws in a neoclassical von Neumann model," Journal of Mathematical Economics, Elsevier, vol. 24(3), pages 271-280.
    3. Carl Chiarella & Mark Craddock & Nadima El-Hassan, 2003. "An Implementation of Bouchouev's Method for a Short Time Calibration of Option Pricing Models," Computational Economics, Springer;Society for Computational Economics, vol. 22(2), pages 113-138, October.
    4. Adrian Dragulescu & Victor M. Yakovenko, 2000. "Statistical mechanics of money," Papers cond-mat/0001432, arXiv.org, revised Aug 2000.
    5. C. F. Lo & C. H. Hui, 2001. "Valuation of financial derivatives with time-dependent parameters: Lie-algebraic approach," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 73-78.
    6. Lloyd Blenman & Steven Clark, 2005. "Options with Constant Underlying Elasticity in Strikes," Review of Derivatives Research, Springer, vol. 8(2), pages 67-83, August.
    7. Sato, Ryuzo, 2004. "Economic conservation laws as indices of corporate performance," Japan and the World Economy, Elsevier, vol. 16(3), pages 247-267, August.
    8. Samuelson, Paul A., 2004. "Conservation laws in economics," Japan and the World Economy, Elsevier, vol. 16(3), pages 243-246, August.
    9. Rama Cont, 2008. "Frontiers in Quantitative Finance: credit risk and volatility modeling," Post-Print hal-00437588, HAL.
    10. Mitchell, Thomas, 2004. "Conservation laws for microeconomists!: Comments on "Economic Conservation Laws as Indices of Corporate Performance" by Ryuzo Sato," Japan and the World Economy, Elsevier, vol. 16(3), pages 269-276, August.
    11. Wilmott,Paul & Howison,Sam & Dewynne,Jeff, 1995. "The Mathematics of Financial Derivatives," Cambridge Books, Cambridge University Press, number 9780521497893, November.
    12. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Philippe Jacquinot & Nikolay Sukhomlin, 2010. "A direct formulation of implied volatility in the Black- Scholes model," Post-Print hal-02527822, HAL.
    2. Zhaoyuan Li & Maozai Tian, 2017. "A New Method For Dynamic Stock Clustering Based On Spectral Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 50(3), pages 373-392, October.
    3. Assaf Almog & Ferry Besamusca & Mel MacMahon & Diego Garlaschelli, 2015. "Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-16, July.
    4. Paulo Ferreira & Éder J.A.L. Pereira & Hernane B.B. Pereira, 2020. "From Big Data to Econophysics and Its Use to Explain Complex Phenomena," JRFM, MDPI, vol. 13(7), pages 1-10, July.
    5. Pištěk, Miroslav & Slanina, František, 2011. "Diversity of scales makes an advantage: The case of the Minority Game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(13), pages 2549-2561.
    6. Denis S. Grebenkov & Jeremy Serror, 2014. "Optimal Allocation of Trend Following Strategies," Papers 1410.8409, arXiv.org.
    7. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    8. Sebastien Valeyre, 2022. "Optimal trend following portfolios," Papers 2201.06635, arXiv.org.
    9. Kiran Sharma & Parul Khurana, 2021. "Growth and dynamics of Econophysics: a bibliometric and network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4417-4436, May.
    10. Xinyu Wang & Liang Zhao & Ning Zhang & Liu Feng & Haibo Lin, 2022. "Stability of China's Stock Market: Measure and Forecast by Ricci Curvature on Network," Papers 2204.06692, arXiv.org.
    11. Anirban Chakraborti & Kiran Sharma & Hirdesh K. Pharasi & Sourish Das & Rakesh Chatterjee & Thomas H. Seligman, 2018. "Characterization of catastrophic instabilities: Market crashes as paradigm," Papers 1801.07213, arXiv.org.
    12. Dimitri O. Ledenyov & Viktor O. Ledenyov, 2013. "On the optimal allocation of assets in investment portfolio with application of modern portfolio and nonlinear dynamic chaos theories in investment, commercial and central banks," Papers 1301.4881, arXiv.org, revised Feb 2013.
    13. Michele Vodret & Iacopo Mastromatteo & Bence Tóth & Michael Benzaquen, 2022. "Microfounding GARCH Models and Beyond: A Kyle-inspired Model with Adaptive Agents," Working Papers hal-03797251, HAL.
    14. Miroslav Piv{s}tv{e}k & Frantisek Slanina, 2014. "Diversity of scales makes an advantage: The case of the Minority Game," Papers 1401.4331, arXiv.org.
    15. Sieczka, Paweł & Hołyst, Janusz A., 2008. "Statistical properties of short term price trends in high frequency stock market data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(5), pages 1218-1224.
    16. D. Sornette, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based models," Papers 1404.0243, arXiv.org.
    17. Stefan, F.M. & Atman, A.P.F., 2015. "Is there any connection between the network morphology and the fluctuations of the stock market index?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 630-641.
    18. Heckens, Anton J. & Guhr, Thomas, 2022. "New collectivity measures for financial covariances and correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    19. Ming Xi Huang, 2010. "Modelling Default Correlations in a Two-Firm Model with Dynamic Leverage Ratios," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2010, March.
    20. Pan, Raj Kumar & Sinha, Sitabhra, 2008. "Inverse-cubic law of index fluctuation distribution in Indian markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2055-2065.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-02533014. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.