Improving the pricing of options: A neural network approach

In this paper we apply statistical inference techniques to build neural net work models which are able to explain the prices of call options written on the German stock index DAX. By testing for the explanatory power of severa l variables serving as network inputs, some insight into the pricing proces s of the option market is obtained. The results indicate that statistical s pecification strategies lead to parsimonious networks which have a superior out-of-sample performance when compared to the Black/Scholes model. We fur ther Validate our results by providing plausible hedge parameters. (C) 1998 John Wiley & Sons, Ltd.