New simulation methodology for risk analysis: genetic programming with monte carlo simulation for option pricing

ABSTRACT

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.

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