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Adapting Black-Scholes to a non-Black-Scholes environment viagenetic programming
Chidambaran, N.K. Lee, C.W.J. Trigueros, J.R.
Sch. of Bus., Tulane Univ., New Orleans, LA;

This paper appears in: Computational Intelligence for Financial Engineering (CIFEr), 1998. Proceedings of the IEEE/IAFE/INFORMS 1998 Conference on
Publication Date: 29-31 Mar 1998
On page(s): 197-211
Meeting Date: 03/29/1998 - 03/31/1998
Location: New York, NY, USA
ISBN: 0-7803-4930-X
References Cited: 36
INSPEC Accession Number: 5985997
Digital Object Identifier: 10.1109/CIFER.1998.690105
Current Version Published: 2002-08-06

Abstract
The authors propose a new methodology that uses genetic programming to approximate the relationship between option price, the terms of the option contract, and properties of the underlying stock price. A crucial advantage of the genetic programming approach is that one can include the Black-Scholes formula in the parameter set, which allows one to search for an approximation better than currently known formulae. Using Monte Carlo simulations, they show that when data is generated using a jump-diffusion process, genetic programming approximates the true solution better than Black-Scholes. Other advantages to the approach are its low demand for data and its computational speed

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