Genetic programming with Monte Carlo simulation for option pricing
Chidambaran, N.K.
Rutgers Bus. Sch., Rutgers Univ., Piscataway, NJ, USA;
This paper appears in: Simulation Conference, 2003. Proceedings of the 2003 Winter
Publication Date: 7-10 Dec. 2003
Volume: 1,
On page(s): 285- 292 Vol.1
ISBN: 0-7803-8131-9
INSPEC Accession Number: 7947494
Current Version Published: 2004-01-30
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.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
