Evolving fuzzy systems for pricing fixed income options

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 pricing data. Evolving models are particularly appropriate since it gradually develops the model structure and its 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 option estimation. In particular, we emphasize the use of the evolving participatory learning method. The model suggested in this paper is compared against the traditional Black closed-form formula, artificial neural networks structures and alternative evolving fuzzy system approaches. Actual daily data used in the experiments cover the period from January 2003 to June 2008. We measure forecast performance of all models based on summary measures of forecast accuracy and statistical tests for competing models. The results show that the evolving fuzzy system model is effective especially for out-of-the-money options.