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Computational Economics

, Volume 27, Issue 2–3, pp 329–351 | Cite as

Robust Artificial Neural Networks for Pricing of European Options

  • Panayiotis C. Andreou
  • Chris Charalambous
  • Spiros H. Martzoukos
Article

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.

Keywords

artificial neural networks huber function implied parameters option pricing & trading robust estimation 

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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Panayiotis C. Andreou
    • 1
  • Chris Charalambous
    • 1
  • Spiros H. Martzoukos
    • 1
  1. 1.Department of Public and Business AdministrationUniversity of CyprusNicosiaCyprus

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