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Deep learning calibration of option pricing models: some pitfalls and solutions

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  • A Itkin

Abstract

Recent progress in the field of artificial intelligence, machine learning and also in computer industry resulted in the ongoing boom of using these techniques as applied to solving complex tasks in both science and industry. Same is, of course, true for the financial industry and mathematical finance. In this paper we consider a classical problem of mathematical finance - calibration of option pricing models to market data, as it was recently drawn some attention of the financial society in the context of deep learning and artificial neural networks. We highlight some pitfalls in the existing approaches and propose resolutions that improve both performance and accuracy of calibration. We also address a problem of no-arbitrage pricing when using a trained neural net, that is currently ignored in the literature.

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  • A Itkin, 2019. "Deep learning calibration of option pricing models: some pitfalls and solutions," Papers 1906.03507, arXiv.org.
    • Handle: RePEc:arx:papers:1906.03507
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    Cited by:

    1. Marc Sabate-Vidales & David v{S}iv{s}ka & Lukasz Szpruch, 2020. "Solving path dependent PDEs with LSTM networks and path signatures," Papers 2011.10630, arXiv.org.
    2. Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021. "Black-box model risk in finance," Papers 2102.04757, arXiv.org.
    3. Peter Carr & Andrey Itkin & Sasha Stoikov, 2019. "A model-free backward and forward nonlinear PDEs for implied volatility," Papers 1907.07305, arXiv.org.
    4. Kentaro Hoshisashi & Carolyn E. Phelan & Paolo Barucca, 2023. "No-Arbitrage Deep Calibration for Volatility Smile and Skewness," Papers 2310.16703, arXiv.org, revised Jan 2024.
    5. Weilong Fu & Ali Hirsa, 2022. "Solving barrier options under stochastic volatility using deep learning," Papers 2207.00524, arXiv.org.
    6. Muyang Ge & Shen Zhou & Shijun Luo & Boping Tian, 2021. "3D Tensor-based Deep Learning Models for Predicting Option Price," Papers 2106.02916, arXiv.org, revised Sep 2021.
    7. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    8. Fabio Baschetti & Giacomo Bormetti & Pietro Rossi, 2023. "Deep calibration with random grids," Papers 2306.11061, arXiv.org, revised Jan 2024.
    9. Kentaro Hoshisashi & Carolyn E. Phelan & Paolo Barucca, 2024. "Whack-a-mole Online Learning: Physics-Informed Neural Network for Intraday Implied Volatility Surface," Papers 2411.02375, arXiv.org.
    10. Marc Chataigner & Stéphane Crépey & Matthew Dixon, 2020. "Deep Local Volatility," Risks, MDPI, vol. 8(3), pages 1-18, August.
    11. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2021. "Arbitrage-free neural-SDE market models," Papers 2105.11053, arXiv.org, revised Aug 2021.
    12. Marc Chataigner & St'ephane Cr'epey & Matthew Dixon, 2020. "Deep Local Volatility," Papers 2007.10462, arXiv.org.
    13. Andrew Na & Meixin Zhang & Justin Wan, 2023. "Computing Volatility Surfaces using Generative Adversarial Networks with Minimal Arbitrage Violations," Papers 2304.13128, arXiv.org, revised Dec 2023.
    14. Ali Hirsa & Weilong Fu, 2020. "An unsupervised deep learning approach in solving partial integro-differential equations," Papers 2006.15012, arXiv.org, revised Dec 2020.

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