Abstract
We propose a methodology of Genetic Programming to approximate the relationship between the option price, its contract terms and the properties of the underlying stock price. An important advantage of the Genetic Programming approach is that we can incorporate currently known formulas, such as the Black-Scholes model, in the search for the best approximation to the true pricing formula. Using Monte Carlo simulations, we show that the Genetic Programming model approximates the true solution better than the Black-Scholes model when stock prices follow a jump-diffusion process. We also show that the Genetic Programming model outperforms various other models when pricing options in the real world. Other advantages of the Genetic Programming approach include its low demand for data, and its computational speed.
Published previously in: Computational Finance — Proceedings of the Sixth International Conference, Leonard N. Stern School of Business, January 1999. MIT Press, Cambridge, MA
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© 2002 Springer-Verlag Berlin Heidelberg
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Chidambaran, N., Triqueros, J., Lee, CW.J. (2002). Option Pricing Via Genetic Programming. In: Chen, SH. (eds) Evolutionary Computation in Economics and Finance. Studies in Fuzziness and Soft Computing, vol 100. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1784-3_20
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DOI: https://doi.org/10.1007/978-3-7908-1784-3_20
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2512-1
Online ISBN: 978-3-7908-1784-3
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