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Improving the pricing of options: a neural network approach
Author(s) -
Anders Ulrich,
Korn Olaf,
Schmitt Christian
Publication year - 1998
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(1998090)17:5/6<369::aid-for702>3.0.co;2-s
Subject(s) - artificial neural network , computer science , econometrics , black–scholes model , statistical inference , hedge , inference , valuation of options , stock market index , stock market , economics , artificial intelligence , statistics , mathematics , volatility (finance) , ecology , paleontology , horse , biology
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 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. Copyright © 1998 John Wiley & Sons, Ltd.