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Robust Artificial Neural Networks for Pricing of European Options

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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.

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Authors and Affiliations

  1. Department of Public and Business Administration, University of Cyprus, P.O. Box 20537, CY 1678, Nicosia, Cyprus

    Panayiotis C. Andreou, Chris Charalambous & Spiros H. Martzoukos

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  1. Panayiotis C. Andreou
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  2. Chris Charalambous
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  3. Spiros H. Martzoukos
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Correspondence to Chris Charalambous.

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JEL Classification: G13, G14

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Andreou, P.C., Charalambous, C. & Martzoukos, S.H. Robust Artificial Neural Networks for Pricing of European Options. Comput Econ 27, 329–351 (2006). https://doi.org/10.1007/s10614-006-9030-x

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  • DOI: https://doi.org/10.1007/s10614-006-9030-x

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