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Considering Appropriate Input Features of Neural Network to Calibrate Option Pricing Models

Author

Listed:
  • Hyun-Gyoon Kim

    (Ajou University)

  • Hyeongmi Kim

    (Nice Pricing & Information)

  • Jeonggyu Huh

    (Sungkyunkwan University)

Abstract

Parameter estimation is crucial in using option pricing models, but it is often an ill-conditioned problem. While it has been demonstrated that neural networks can enhance the efficiency of multiple tasks, when performing parameter estimation using option prices data, the neural network approaches are fundamentally vulnerable because the task is one of the ill-conditioned problems. To address the issue, we propose a bijective transformation of the input features of a neural network to transform the ill-conditioned problem into an equivalent well-conditioned problem. This transformation can be simply summarized as using the corresponding implied volatilities as input features instead of option prices. Experiments have shown that the estimation network that use the transformed values as network inputs have significantly improved efficiency compared to the network that use the original values.

Suggested Citation

  • Hyun-Gyoon Kim & Hyeongmi Kim & Jeonggyu Huh, 2025. "Considering Appropriate Input Features of Neural Network to Calibrate Option Pricing Models," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 77-104, July.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10686-2
    DOI: 10.1007/s10614-024-10686-2
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    References listed on IDEAS

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    1. Hsu, Y.L. & Lin, T.I. & Lee, C.F., 2008. "Constant elasticity of variance (CEV) option pricing model: Integration and detailed derivation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(1), pages 60-71.
    2. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    3. Shuaiqiang Liu & Anastasia Borovykh & Lech A. Grzelak & Cornelis W. Oosterlee, 2019. "A neural network-based framework for financial model calibration," Papers 1904.10523, arXiv.org.
    4. 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.
    5. 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.
    6. Patrick Büchel & Michael Kratochwil & Maximilian Nagl & Daniel Rösch, 2022. "Deep calibration of financial models: turning theory into practice," Review of Derivatives Research, Springer, vol. 25(2), pages 109-136, July.
    7. repec:bla:jfinan:v:44:y:1989:i:1:p:211-19 is not listed on IDEAS
    8. Jim Gatheral & Antoine Jacquier, 2014. "Arbitrage-free SVI volatility surfaces," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 59-71, January.
    9. 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.
    10. Henry Stone, 2020. "Calibrating rough volatility models: a convolutional neural network approach," Quantitative Finance, Taylor & Francis Journals, vol. 20(3), pages 379-392, March.
    11. Ballestra, Luca Vincenzo & Cecere, Liliana, 2016. "A numerical method to estimate the parameters of the CEV model implied by American option prices: Evidence from NYSE," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 100-106.
    12. Blanka Horvath & Aitor Muguruza & Mehdi Tomas, 2021. "Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models," Quantitative Finance, Taylor & Francis Journals, vol. 21(1), pages 11-27, January.
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