This investigation attempts to achieve two objectives. The first aim is to study the relationship between the TAIEX call options market and the spot market among different levels of Moneyness, namely, deep-in-the-money, in-the-money, at-the-money, deep-out-of-the-money, as well as out-of-the-money. The other one is to build a pricing model of TAIEX call options. The experimental data presented in this study come from the daily closing transaction price of TAIEX call options and the associated spot market from September 24, 2001 to August 31, 2003. This investigation applied the Bi_EGARCH model to study the interactive relationship of returns and volatility between TAIEX call options and the spot market, and used the Neuron Algorithm to establish a model for pricing TAIEX call options. This study reaches two main findings. First, the interactive relationship between TAIEX call options and the spot market differs among different Moneyness, and investors take larger risks under out-of-the-money and deep-out-of-money situations. Second, the prediction ability of the neural network pricing model is better than that of the regression model given different levels of Moneyness in most circumstances.