  The purpose of this paper is to improve the accuracy of dynamic hedging using
implied volatilities generated by genetic programming. Using real data from
S&P500 index options, the genetic programming's ability to forecast Black and
Scholes implied volatility is compared between static and dynamic
training-subset selection methods. The performance of the best generated GP
implied volatilities is tested in dynamic hedging and compared with
Black-Scholes model. Based on MSE total, the dynamic training of GP yields
better results than those obtained from static training with fixed samples.
According to hedging errors, the GP model is more accurate almost in all
hedging strategies than the BS model, particularly for in-the-money call
options and at-the-money put options.
