Comparing Genetic Adaptive Neural Network and Black-Scholes for the Pricing and Hedging of Options

Neural networks have the ability of learning and performing high-speed calculations, also with nonlinear processing and tolerance of faults, its prediction faculty becomes quite outstanding. Although most literature is available on options pricing via neutral networks, little attention has been paid to hedging. This study applies the genetic adaptive neural network to the pricing and hedging of warrants via utilizing the pattern of specific warrants time value and 'Delta' behavior. The empirical results indicate that the method based on neural networks excels the BS model in interpretive capability and error degrees on pricing, risk exposure and profits on hedging. It means that in the Taiwan warrant market, the proposed model can provide a more accurate pricing and efficient hedging model than the BS model.