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Hybrid artificial neural networks for efficient valuation of real options and financial derivatives

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  • Chris Charalambous
  • Spiros Martzoukos

Abstract

A hybrid valuation methodology is proposed and tested for improving the efficiency of contingent claims pricing by combining Artificial Neural Networks (ANN) and conventional parametric option pricing techniques. With one application on financial derivatives and one on real options the method’s superiority is demonstrated. The resulting efficiency is instrumental for real time applications. Copyright Springer-Verlag Berlin/Heidelberg 2005

Suggested Citation

  • Chris Charalambous & Spiros Martzoukos, 2005. "Hybrid artificial neural networks for efficient valuation of real options and financial derivatives," Computational Management Science, Springer, vol. 2(2), pages 155-161, March.
    • Handle: RePEc:spr:comgts:v:2:y:2005:i:2:p:155-161
    DOI: 10.1007/s10287-004-0032-7
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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. 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.

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