IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v7y2019i4p68-d284016.html
   My bibliography  Save this article

Reverse Engineering of Option Pricing: An AI Application

Author

Listed:
  • Bodo Herzog

    (ESB Business School, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany
    RRI Reutlingen Research Institute, 72762 Reutlingen, Germany)

  • Sufyan Osamah

    (ESB Business School, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany)

Abstract

This paper studies option pricing based on a reverse engineering (RE) approach. We utilize artificial intelligence in order to numerically compute the prices of options. The data consist of more than 5000 call- and put-options from the German stock market. First, we find that option pricing under reverse engineering obtains a smaller root mean square error to market prices. Second, we show that the reverse engineering model is reliant on training data. In general, the novel idea of reverse engineering is a rewarding direction for future research. It circumvents the limitations of finance theory, among others strong assumptions and numerical approximations under the Black–Scholes model.

Suggested Citation

  • Bodo Herzog & Sufyan Osamah, 2019. "Reverse Engineering of Option Pricing: An AI Application," IJFS, MDPI, vol. 7(4), pages 1-12, November.
    • Handle: RePEc:gam:jijfss:v:7:y:2019:i:4:p:68-:d:284016
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/7/4/68/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7072/7/4/68/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Charles J. Corrado & Tie Su, 1996. "Skewness And Kurtosis In S&P 500 Index Returns Implied By Option Prices," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 19(2), pages 175-192, June.
    2. Andreou, Panayiotis C. & Charalambous, Chris & Martzoukos, Spiros H., 2008. "Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1415-1433, March.
    3. Yao, Jingtao & Li, Yili & Tan, Chew Lim, 2000. "Option price forecasting using neural networks," Omega, Elsevier, vol. 28(4), pages 455-466, August.
    4. Richard H. Thaler & Amos Tversky & Daniel Kahneman & Alan Schwartz, 1997. "The Effect of Myopia and Loss Aversion on Risk Taking: An Experimental Test," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 647-661.
    5. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    6. Bodo Herzog, 2016. "Modelling Monetary and Fiscal Governance in the Wake of the Sovereign Debt Crisis in Europe," Economies, MDPI, vol. 4(2), pages 1-11, May.
    7. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    8. Bakshi, Gurdip & Cao, Charles & Chen, Zhiwu, 1997. "Empirical Performance of Alternative Option Pricing Models," Journal of Finance, American Finance Association, vol. 52(5), pages 2003-2049, December.
    9. repec:dau:papers:123456789/13809 is not listed on IDEAS
    10. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    11. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    12. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    13. Sofiane Aboura, 2013. "Empirical Performance Study of Alternative Option Pricing Models: An Application to the French Option Market," Post-Print hal-01531319, HAL.
    14. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    15. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
    16. Charles J. Corrado & Tie Su, 1996. "Skewness And Kurtosis In S&P 500 Index Returns Implied By Option Prices," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 19(2), pages 175-192, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Florin Popișter & Daniela Popescu & Ancuţa Păcurar & Răzvan Păcurar, 2021. "Mathematical Approach in Complex Surfaces Toolpaths," Mathematics, MDPI, vol. 9(12), pages 1-13, June.
    2. Florin Turcaș & Florin Cornel Dumiter & Marius Boiță, 2022. "Econophysics Techniques and Their Applications on the Stock Market," Mathematics, MDPI, vol. 10(6), pages 1-25, March.

    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. Jurczenko, Emmanuel & Maillet, Bertrand & Negrea, Bogdan, 2002. "Revisited multi-moment approximate option pricing models: a general comparison (Part 1)," LSE Research Online Documents on Economics 24950, London School of Economics and Political Science, LSE Library.
    2. Cao, Yi & Liu, Xiaoquan & Zhai, Jia, 2021. "Option valuation under no-arbitrage constraints with neural networks," European Journal of Operational Research, Elsevier, vol. 293(1), pages 361-374.
    3. Pena, Ignacio & Rubio, Gonzalo & Serna, Gregorio, 1999. "Why do we smile? On the determinants of the implied volatility function," Journal of Banking & Finance, Elsevier, vol. 23(8), pages 1151-1179, August.
    4. Bogdan Negrea & Bertrand Maillet & Emmanuel Jurczenko, 2002. "Revisited Multi-moment Approximate Option," FMG Discussion Papers dp430, Financial Markets Group.
    5. Sol Kim, 2021. "Portfolio of Volatility Smiles versus Volatility Surface: Implications for pricing and hedging options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(7), pages 1154-1176, July.
    6. Don M. Chance & Thomas A. Hanson & Weiping Li & Jayaram Muthuswamy, 2017. "A bias in the volatility smile," Review of Derivatives Research, Springer, vol. 20(1), pages 47-90, April.
    7. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    8. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    9. Yanhui Shen, 2023. "American Option Pricing using Self-Attention GRU and Shapley Value Interpretation," Papers 2310.12500, arXiv.org.
    10. Andreou, Panayiotis C. & Charalambous, Chris & Martzoukos, Spiros H., 2008. "Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1415-1433, March.
    11. Yao Wang & Jingmei Zhao & Qing Li & Xiangyu Wei, 2024. "Considering momentum spillover effects via graph neural network in option pricing," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(6), pages 1069-1094, June.
    12. Ryszard Kokoszczyński & Paweł Sakowski & Robert Ślepaczuk, 2017. "Which Option Pricing Model Is the Best? HF Data for Nikkei 225 Index Options," Central European Economic Journal, Sciendo, vol. 4(51), pages 18-39, December.
    13. Fiorentini, Gabriele & Leon, Angel & Rubio, Gonzalo, 2002. "Estimation and empirical performance of Heston's stochastic volatility model: the case of a thinly traded market," Journal of Empirical Finance, Elsevier, vol. 9(2), pages 225-255, March.
    14. Chauveau, Thierry & Gatfaoui, Hayette, 2002. "Systematic risk and idiosyncratic risk: a useful distinction for valuing European options," Journal of Multinational Financial Management, Elsevier, vol. 12(4-5), pages 305-321.
    15. Maciej Wysocki & Robert Ślepaczuk, 2020. "Artificial Neural Networks Performance in WIG20 Index Options Pricing," Working Papers 2020-19, Faculty of Economic Sciences, University of Warsaw.
    16. Andreou, Panayiotis C. & Charalambous, Chris & Martzoukos, Spiros H., 2010. "Generalized parameter functions for option pricing," Journal of Banking & Finance, Elsevier, vol. 34(3), pages 633-646, March.
    17. Liu, Xiaoquan & Cao, Yi & Ma, Chenghu & Shen, Liya, 2019. "Wavelet-based option pricing: An empirical study," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1132-1142.
    18. Ke Nian & Thomas F. Coleman & Yuying Li, 2018. "Learning minimum variance discrete hedging directly from the market," Quantitative Finance, Taylor & Francis Journals, vol. 18(7), pages 1115-1128, July.
    19. Jeonggyu Huh, 2018. "Pricing Options with Exponential Levy Neural Network," Papers 1802.06520, arXiv.org, revised Sep 2018.
    20. René Garcia & Richard Luger & Eric Renault, 2000. "Asymmetric Smiles, Leverage Effects and Structural Parameters," Working Papers 2000-57, Center for Research in Economics and Statistics.

    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:gam:jijfss:v:7:y:2019:i:4:p:68-:d:284016. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.