Pricing financial derivatives with neural networks

Marco J Morelli, Guido Montagna, Oreste Nicrosini, Michele Treccani, Marco Farina and Paolo Amato

Physica A: Statistical Mechanics and its Applications, 2004, vol. 338, issue 1, 160-165

Abstract: Neural network algorithms are applied to the problem of option pricing and adopted to simulate the nonlinear behavior of such financial derivatives. Two different kinds of neural networks, i.e. multi-layer perceptrons and radial basis functions, are used and their performances compared in detail. The analysis is carried out both for standard European options and American ones, including evaluation of the Greek letters, necessary for hedging purposes. Detailed numerical investigation show that, after a careful phase of training, neural networks are able to predict the value of options and Greek letters with high accuracy and competitive computational time.

Keywords: Econophysics; Option pricing; Neural networks (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:338:y:2004:i:1:p:160-165

DOI: 10.1016/j.physa.2004.02.038

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