Original Paper

Evolving Systems

, Volume 3, Issue 1, pp 5-18

Evolving fuzzy systems for pricing fixed income options

  • Leandro MacielAffiliated withSchool of Electrical and Computer Engineering, University of Campinas
  • , Andre LemosAffiliated withDepartment of Electronic Engineering, Federal University of Minas Gerais
  • , Fernando GomideAffiliated withSchool of Electrical and Computer Engineering, University of Campinas
  • , Rosangela BalliniAffiliated withInstitute of Economics, University of Campinas Email author 

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During the recent decades, option pricing became an important topic in computational finance. The main issue is to obtain a model of option prices that reflects price movements observed in the real world. In this paper we address option pricing using an evolving fuzzy system model and Brazilian interest rate options data. Evolving models are particularly appropriate because they gradually develops the model structure and parameters from a stream of data. Therefore, evolving fuzzy models provide a higher level of system adaptation and learns the system dynamics continuously, an essential attribute in pricing options estimation. In particular, we emphasize the use of the evolving participatory learning methods. The participatory evolving models considered in this paper are compared against the traditional Black’s closed-form formula, artificial neural networks structures, and alternative evolving fuzzy system approaches reported in the literature. Actual daily data used in the experiments cover the period from January 2003 to June 2008. We measure forecast performance of all models and report the statistical tests done for the competing forecast models. The results show that the participatory evolving fuzzy system modeling approach is effective to estimate prices of fixed income options.


Evolving fuzzy systems Option pricing Neural networks Interest rate Derivatives