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Pricing financial derivatives with neural networks

Author

Listed:
  • Morelli, Marco J
  • Montagna, Guido
  • Nicrosini, Oreste
  • Treccani, Michele
  • Farina, Marco
  • Amato, Paolo

Abstract

Neural network algorithms are applied to the problem of option pricing and adopted to simulate the nonlinear behavior of such financial derivatives. Two different kinds of neural networks, i.e. multi-layer perceptrons and radial basis functions, are used and their performances compared in detail. The analysis is carried out both for standard European options and American ones, including evaluation of the Greek letters, necessary for hedging purposes. Detailed numerical investigation show that, after a careful phase of training, neural networks are able to predict the value of options and Greek letters with high accuracy and competitive computational time.

Suggested Citation

  • Morelli, Marco J & Montagna, Guido & Nicrosini, Oreste & Treccani, Michele & Farina, Marco & Amato, Paolo, 2004. "Pricing financial derivatives with neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 160-165.
    • Handle: RePEc:eee:phsmap:v:338:y:2004:i:1:p:160-165
    DOI: 10.1016/j.physa.2004.02.038
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    References listed on IDEAS

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    1. 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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    Cited by:

    1. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    2. Efe Arin & A. Murat Ozbayoglu, 2022. "Deep Learning Based Hybrid Computational Intelligence Models for Options Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 39-58, January.
    3. Fei Chen & Charles Sutcliffe, 2012. "Pricing And Hedging Short Sterling Options Using Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 128-149, April.
    4. Chih-Chung Yang & Yungho Leu & Chien-Pang Lee, 2014. "A Dynamic Weighted Distancedbased Fuzzy Time Series Neural Network with Bootstrap Model for Option Price Forecasting," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 115-129, June.

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