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Pricing Options in Hong Kong Market Based on Neural Networks

  • Xun Liang
  • Haisheng Zhang
  • Jian Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

Option pricing is one of the important issues in the financial industry and has been studied for decades. Many classical and successful pricing models have been presented to implement the pricing processing either by numerical computing or by simulation. In this paper, a new option pricing model based on a three-layer feedforward neural network is established to improve the pricing performance. The new model combines 4 traditional pricing models to obtain a better forecasting result based on learning and cutting down their forecasting errors. Numerical experiments are conducted on the data of Hong Kong option market from March 2005 to July 2005. The new model improves the pricing performance remarkably compared to the traditional option pricing models.

Keywords

Monte Carlo Forecast Error Option Price Hide Neuron Neutral Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xun Liang
    • 1
    • 2
  • Haisheng Zhang
    • 1
  • Jian Yang
    • 1
  1. 1.Institute of Computer Science and TechnologyPeking UniversityBeijingChina
  2. 2.Department of Economics and Operations ResearchStanford UniversityUSA

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