Option Pricing with Genetic Programming
Shu‐Heng Chen, C.-H. Yeh, W.-C. Lee · 1998
One of the most recent applications of GP to finance is to use genetic programming to derive option pricing formulas. Earlier studies take the Black-Scholes model as the true model and use the artificial data generated by it to train and to test GP. This paper may be regarded as the first attempt to provide some initial evidence of the empirical relevance of GP to option pricing. By using the real data from S&P 500 index options, we train and test two styles of GP, one-stage GP which does not distinguish the case in-the-money from the case out-of-the-money, and two-stage GP, which does. The GP pricing formulas derived are then compared with the Black-Scholes formulas, linear regression models and feedforward neural nets. Based on the post-sample performance, it is found that while the Black-Scholes model still takes the lead, twostage GP can outperform the rest of the models in our limited experiments. From this exercise, we summarize a few features which may make GP a promising tool in f...