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Confidence limits for data mining models of options prices

Author

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  • Healy, J.V.
  • Dixon, M.
  • Read, B.J.
  • Cai, F.F.

Abstract

Non-parametric methods such as artificial neural nets can successfully model prices of financial options, out-performing the Black–Scholes analytic model (Eur. Phys. J. B 27 (2002) 219). However, the accuracy of such approaches is usually expressed only by a global fitting/error measure. This paper describes a robust method for determining prediction intervals for models derived by non-linear regression. We have demonstrated it by application to a standard synthetic example (29th Annual Conference of the IEEE Industrial Electronics Society, Special Session on Intelligent Systems, pp. 1926–1931). The method is used here to obtain prediction intervals for option prices using market data for LIFFE “ESX” FTSE 100 index options (http://www.liffe.com/liffedata/contracts/month_onmonth.xls). We avoid special neural net architectures and use standard regression procedures to determine local error bars. The method is appropriate for target data with non constant variance (or volatility).

Suggested Citation

  • Healy, J.V. & Dixon, M. & Read, B.J. & Cai, F.F., 2004. "Confidence limits for data mining models of options prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 162-167.
    • Handle: RePEc:eee:phsmap:v:344:y:2004:i:1:p:162-167
    DOI: 10.1016/j.physa.2004.06.112
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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.
    2. Bennell, J. & Sutcliffe, C., 2000. "Black-Scholes Versus Neural Networks in Pricing FTSE 100 Options," Papers 00-156, University of Southampton - Department of Accounting and Management Science.
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    Citations

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    Cited by:

    1. Healy, Jerome V. & Dixon, Maurice & Read, Brian J. & Cai, Fang Fang, 2007. "Non-parametric extraction of implied asset price distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 121-128.
    2. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    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.

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