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Evolutionary Computation in Option Pricing: Determining Implied Volatilities Based on American Put Options

  • Christian Keber
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 100)

Abstract

In our investigation, Genetic Programming (GP) is used in the context of option pricing. Up to now very few publications exist in this field, the first papers, to our knowledge, come from Chen et al., Chidambaran et al., and Keber [13,14,24,25]. Currently, research activities are increasing in the “GP/Finance”-area, as, e.g., the publications of Allen and Karjalainen as well as Neely et al. show [2,31]. In the context of option valuation a lot of models are not amenable to an exact analytical solution. Such models must be solved either by using numerical procedures or analytical approximations. In our paper we derive analytical approximations for calculating implied volatilities based on American put options using Genetic Programming. Applying our approximations to experimental data sets we can show that the results obtained by our formulas are very close to the numerically calculated ones.

Keywords

Genetic Program Option Price Stock Prex Implied Volatility Mean Absolute Deviation 
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-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Christian Keber
    • 1
  1. 1.Department of Business AdministrationUniversity of ViennaAustria

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