Neural Networks for Contingent Claim Pricing via the Galerkin Method

  • Emilio Barucci
  • Umberto Cherubini
  • Leonardo Landi
Chapter
Part of the Advances in Computational Economics book series (AICE, volume 6)

Abstract

We use Neural Networks as a Semi-NonParametric technique to approximate, by means of the Galerkin method, contingent claim prices defined by a no-arbitrage Partial Differential Equation. The Neural Networks’ weights are determined as to satisfy the no-arbitrage Partial Differential Equation. A general solution procedure is developed for European Contingent Claims. The main feature of the Neural Network is that its weights are time varying, they change as the time to expiration of the claim changes. The method has been evaluated for option pricing in the standard Black and Scholes framework.

Keywords

Asset Price Galerkin Method Option Price Trial Function Implied Volatility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer Science+Business Media Dordrecht 1997

Authors and Affiliations

  • Emilio Barucci
  • Umberto Cherubini
  • Leonardo Landi

There are no affiliations available

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