Improving the pricing of options: a neural network approach

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: ZEW Discussion Papers ; No. 96-04

Classification and Topics

• Classification: Wirtschaft
• Subject: Option Pricing ; Neural Networks ; Statistical Inference ; Model Selection ; Optionspreistheorie ; Neuronale Netze ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Anders, Ulrich ; Korn, Olaf ; Schmitt, Christian
• Event: Veröffentlichung
• (who): Zentrum für Europäische Wirtschaftsforschung (ZEW) ; ZBW – Leibniz Information Centre for Economics
• (where): Mannheim
• (when): 1996

Further information

• Handle: http://hdl.handle.net/10419/29448
• Last update: 10.03.2025, 11:43 AM CET