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A Comparison of Artificial Neural Networks and Bootstrap Aggregating Ensembles in a Modern Financial Derivative Pricing Framework

Author

Listed:
  • Ryno du Plooy

    (Department of Finance and Investment Management, University of Johannesburg, P.O. Box 524, Auckland Park 2006, South Africa)

  • Pierre J. Venter

    (Department of Finance and Investment Management, University of Johannesburg, P.O. Box 524, Auckland Park 2006, South Africa)

Abstract
In this paper, the pricing performances of two learning networks, namely an artificial neural network and a bootstrap aggregating ensemble network, were compared when pricing the Johannesburg Stock Exchange (JSE) Top 40 European call options in a modern option pricing framework using a constructed implied volatility surface. In addition to this, the numerical accuracy of the better performing network was compared to a Monte Carlo simulation in a separate numerical experiment. It was found that the bootstrap aggregating ensemble network outperformed the artificial neural network and produced price estimates within the error bounds of a Monte Carlo simulation when pricing derivatives in a multi-curve framework setting.

Suggested Citation

  • Ryno du Plooy & Pierre J. Venter, 2021. "A Comparison of Artificial Neural Networks and Bootstrap Aggregating Ensembles in a Modern Financial Derivative Pricing Framework," JRFM, MDPI, vol. 14(6), pages 1-18, June.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:6:p:254-:d:570259
    as

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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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