Abstract: Implied volatility is the volatility implied by an option price observed in the market.The sensitivity of the volatility among varied kinds of option price ise different.In this work,we build hybrid forecasting models combining wavelet neural network with genetic algorithm.Using these models,option partition according to moneyness is applied and weighted implied volatility measures are regarded as input of the neural network.The genetic algorithm is used to determine the optimal weight of the implied volatility among different kinds of option.Case study on Hong Kong derivative market shows that these hybrid models are better than the conventional Black-Scholes model and the other neural network models adopted in this work.

Key words: option pricing, artificial intelligence, Black-Scholes model, moneyness, implied volatility