This master thesis describes how to price options by means of Genetic Programming. The underlying model is the Generalized Autoregressive Conditional Heteroskedastic (GARCH) asset return process. The goal is to find a closed-form solution for the price of European call options where the underlying securities follow a GARCH process. Genetic Programming is used to generate the pricing function from the data. Genetic Programming is a method of producing programs just by defining a problemdependent fitness function. The resulting equation is found via a heuristic algorithm inspired by natural evolution. To ensure that a good configuration setting is used, preliminary testing of many different settings has been done, suggesting that simpler configurations are more successful in this environment. The resulting equation can be used to calculate the price of an option in the given range with minimal errors. This equation is well behaved and can be used in standard spread sheet programs. It offers a wider range of utilization or a higher accuracy, respectively than other existing approaches.
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Genetic Programming contains a great many worked examples and includes a sample computer code that will allow readers to run their own programs.In getting computers to solve problems without being explicitly programmed, Koza stresses two points: that seemingly different problems from a variety of fields can be reformulated as problems of Genetic Programming - Poznań University of Technology Bilbiography Koza, J. R. Genetic Programming: On the Programming of Computers by Means of Natural Selection MIT Press, 1992 A Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4)
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