Pricing options in hong kong market based on neural networks

Authors:
Xun Liang;Haisheng Zhang;Jian Yang
Affiliations:
Institute of Computer Science and Technology, Peking University, Beijing, China and Department of Economics and Operations Research, Stanford University, CA;Institute of Computer Science and Technology, Peking University, Beijing, China;Institute of Computer Science and Technology, Peking University, Beijing, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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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.