Improving the pricing of options: a neural network approach

Ulrich Anders, Olaf Korn and Christian Schmitt

No 96-04, ZEW Discussion Papers from ZEW - Leibniz Centre for European Economic Research

Abstract: In this paper we apply statistical inference techniques to build neural network 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 several input variables serving as network inputs, some insight into the pricing process of the option market is obtained. The results indicate that statistical specification strategies lead to parsimonious networks which have a superior out-of-sample performance when compared to the Black/Scholes model. We further validate our results by providing plausible hedge parameters.

Keywords: Option Pricing; Neural Networks; Statistical Inference; Model Selection (search for similar items in EconPapers)
Date: 1996
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:zewdip:9604

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