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Title: Robust artificial neural networks for pricing of European options
Authors: Andreou, Panayiotis 
Charalambous, Chris 
Martzoukos, Spiros H. 
metadata.dc.contributor.other: Ανδρέου, Παναγιώτης
Major Field of Science: Social Sciences
Field Category: Economics and Business
Keywords: Artificial neural networks;Huber function;Implied parameters;Option pricing & trading;Robust estimation
Issue Date: 11-Apr-2006
Source: Computational Economics, 2006, vol. 27, no. 2-3, pp. 329-351
Volume: 27
Issue: 2-3
Start page: 329
End page: 351
Journal: Computational Economics 
Abstract: The option pricing ability of Robust Artificial Neural Networks optimized with the Huber function is compared against those optimized with Least Squares. Comparison is in respect to pricing European call options on the S&P 500 using daily data for the period April 1998 to August 2001. The analysis is augmented with the use of several historical and implied volatility measures. Implied volatilities are the overall average, and the average per maturity. Beyond the standard neural networks, hybrid networks that directly incorporate information from the parametric model are included in the analysis. It is shown that the artificial neural network models with the use of the Huber function outperform the ones optimized with least squares..
ISSN: 09277099
DOI: 10.1007/s10614-006-9030-x
Rights: © Springer
Type: Article
Affiliation : University of Cyprus 
Appears in Collections:Άρθρα/Articles

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