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Genetic programming with Monte Carlo simulation for option pricing

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1 Author(s)
Chidambaran, N.K. ; Rutgers Bus. Sch., Rutgers Univ., Piscataway, NJ, USA

I examine the role of programming parameters in determining the accuracy of genetic programming for option pricing. I use Monte Carlo simulations to generate stock and option price data needed to develop a genetic option pricing program. I simulate data for two different stock price processes - a geometric Brownian process and a jump-diffusion process. In the jump-diffusion setting, I seed the genetic program with the Black-Scholes equation as a starting approximation. I find that population size, fitness criteria, and the ability to seed the program with known analytical equations, are important determinants of the efficiency of genetic programming.

Published in:

Simulation Conference, 2003. Proceedings of the 2003 Winter  (Volume:1 )

Date of Conference:

7-10 Dec. 2003

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