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Digital contracts-driven method for pricing complex derivatives

Author

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  • J Lu

    (Graduate School of Engineering, Osaka Prefecture University)

  • H Ohta

    (Graduate School of Engineering, Osaka Prefecture University)

Abstract

In this paper, we propose a hybrid method of nonparametric and parametric methods, that is a digital contracts-driven (DCD) method, for pricing various complex options. Differing from general nonparametric data-driven methods, in which usually the observed data are used as training data directly, in the DCD method the European-style digital contracts of the underlying assets are used as basic inputs for a learning network. The digital contracts calculated from the observed data based upon the parametric method are used as hints in the learning process, and then enable the DCD method to have superior pricing accuracy to the common data-driven method in practical applications. Some Monte Carlo simulation experiments are performed and the results demonstrate that the proposed hybrid method not only has the advantages of generality and superior accuracy as the nonparametric method, but also the robust property to financial data with noise as the parametric method.

Suggested Citation

  • J Lu & H Ohta, 2003. "Digital contracts-driven method for pricing complex derivatives," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(9), pages 1002-1010, September.
    • Handle: RePEc:pal:jorsoc:v:54:y:2003:i:9:d:10.1057_palgrave.jors.2601597
    DOI: 10.1057/palgrave.jors.2601597
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    References listed on IDEAS

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    Cited by:

    1. 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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