Black-Scholes Versus Artificial Neural Networks in Pricing FTSE 100 Options

Option Pricing With Modular Neural Networks

“…Table 3 Based on these results, we conclude that the modularity feature of the MNN model plays a vital role in outperforming the rival models. This also accords with some other studies that focused on pricing of certain types of options (i.e., deep-out-of-themoney options) and found that such partitioning of the data increases the pricing accuracy of the NN models Salih, 2003, Bennell andSutcliffe, 2004).…”

A neural network approach to option pricing

“…This approach was also followed by Bennell and Sutcliffe [17]. The input data was partitioned according the moneyness of the options in-the money (ITM), at-the-money (ATM) and outof-the-money (OTM).…”

“…Until the late 1990's research on option pricing with neural networks was limited. To date most research compares the performance of neural networks to the Black-Scholes optionpricing model (Meissner and Kawano [13], Amilon [14], Yao et al [15], Tino et al [16] and Bennell and Sutcliffe [17]). Most research demonstrates comparable or slightly better performance of neural networks to the tradition models.…”

Option valuation based on the neural regression model

“…Hamid et al and Bennell et al use a back-propagation network to forecast the volatility of S&P500 index futures prices. They compare forecasting volatility from neural networks with implied volatility using the BaroneAdesi and Whaley American futures options pricing models (Bennell & Sutcliffe, 2003;Hamid & Iqbal, 2004). Yao et al, use backpropagation neural networks to forecast the option prices of Nikkei 225 index futures and they outperform the BS model (Yao, Li, & Tan, 2000).…”