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Barrier option pricing: modelling with neural nets

Author

Listed:
  • Xu, L.
  • Dixon, M.
  • Eales, B.A.
  • Cai, F.F.
  • Read, B.J.
  • Healy, J.V.

Abstract

We report call option pricing for up-and-out style barrier options through the use of a neural net model. A synthetic data set was constructed from the real LIFFE standard option price data by use of the Rubenstein and Reiner analytic model (Risk September (1991) 28). Unbiased estimates at the 95% confidence level were achieved for realistic barriers (barrier 4% or more above max(S0,X)).

Suggested Citation

  • Xu, L. & Dixon, M. & Eales, B.A. & Cai, F.F. & Read, B.J. & Healy, J.V., 2004. "Barrier option pricing: modelling with neural nets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 289-293.
    • Handle: RePEc:eee:phsmap:v:344:y:2004:i:1:p:289-293
    DOI: 10.1016/j.physa.2004.06.134
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    References listed on IDEAS

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    1. Mark Broadie & Paul Glasserman & Steven Kou, 1997. "A Continuity Correction for Discrete Barrier Options," Mathematical Finance, Wiley Blackwell, vol. 7(4), pages 325-349, October.
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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.

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