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Deeply Learning Derivatives

Author

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  • Ryan Ferguson
  • Andrew Green

Abstract

This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We develop a methodology to randomly generate appropriate training data and explore the impact of several parameters including layer width and depth, training data quality and quantity on model speed and accuracy.

Suggested Citation

  • Ryan Ferguson & Andrew Green, 2018. "Deeply Learning Derivatives," Papers 1809.02233, arXiv.org, revised Oct 2018.
    • Handle: RePEc:arx:papers:1809.02233
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    File URL: http://arxiv.org/pdf/1809.02233
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    References listed on IDEAS

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    1. Raymond Brummelhuis & Zhongmin Luo, 2017. "CDS Rate Construction Methods by Machine Learning Techniques," Papers 1705.06899, arXiv.org.
    2. Lewandowski, Daniel & Kurowicka, Dorota & Joe, Harry, 2009. "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1989-2001, October.
    3. 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.
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    Citations

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    Cited by:

    1. Antoine Jacquier & Emma R. Malone & Mugad Oumgari, 2019. "Stacked Monte Carlo for option pricing," Papers 1903.10795, arXiv.org.
    2. Christian Bayer & Blanka Horvath & Aitor Muguruza & Benjamin Stemper & Mehdi Tomas, 2019. "On deep calibration of (rough) stochastic volatility models," Papers 1908.08806, arXiv.org.
    3. Jaegi Jeon & Kyunghyun Park & Jeonggyu Huh, 2021. "Extensive networks would eliminate the demand for pricing formulas," Papers 2101.09064, arXiv.org.
    4. Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021. "Black-box model risk in finance," Papers 2102.04757, arXiv.org.
    5. Alessandro Gnoatto & Athena Picarelli & Christoph Reisinger, 2020. "Deep xVA solver -- A neural network based counterparty credit risk management framework," Papers 2005.02633, arXiv.org, revised Dec 2022.
    6. Thibault Collin, 2023. "Using Deep Learning to Hedge Rainbow Options," Working Papers hal-04060013, HAL.
    7. 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.
    8. Gan, Lirong & Wang, Huamao & Yang, Zhaojun, 2020. "Machine learning solutions to challenges in finance: An application to the pricing of financial products," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    9. Blanka Horvath & Aitor Muguruza & Mehdi Tomas, 2019. "Deep Learning Volatility," Papers 1901.09647, arXiv.org, revised Aug 2019.
    10. Weilong Fu & Ali Hirsa, 2022. "Solving barrier options under stochastic volatility using deep learning," Papers 2207.00524, arXiv.org.
    11. Pietro Rossi & Flavio Cocco & Giacomo Bormetti, 2020. "Deep learning Profit & Loss," Papers 2006.09955, arXiv.org, revised Aug 2020.
    12. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    13. Brian Ning & Sebastian Jaimungal & Xiaorong Zhang & Maxime Bergeron, 2021. "Arbitrage-Free Implied Volatility Surface Generation with Variational Autoencoders," Papers 2108.04941, arXiv.org, revised Jan 2022.
    14. Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2020. "Accuracy of Deep Learning in Calibrating HJM Forward Curves," Papers 2006.01911, arXiv.org, revised May 2021.
    15. Jay Cao & Jacky Chen & John Hull & Zissis Poulos, 2021. "Deep Learning for Exotic Option Valuation," Papers 2103.12551, arXiv.org, revised Sep 2021.
    16. Jiří Witzany & Milan Fičura, 2023. "Machine Learning Applications to Valuation of Options on Non-liquid Markets," FFA Working Papers 5.001, Prague University of Economics and Business, revised 24 Jan 2023.
    17. Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2021. "Accuracy of deep learning in calibrating HJM forward curves," Digital Finance, Springer, vol. 3(3), pages 209-248, December.
    18. Sebastian Jaimungal, 2022. "Reinforcement learning and stochastic optimisation," Finance and Stochastics, Springer, vol. 26(1), pages 103-129, January.

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