Pricing Options in Hong Kong Market Based on Neural Networks
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.
KeywordsMonte Carlo Forecast Error Option Price Hide Neuron Neutral Network
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