Option pricing using a committee of neural networks and optimized networks

The derivative market has seen tremendous growth in recent times. We look at a particular area of these markets, viz. options. The pricing of options has its roots in stochastic mathematics since option pricing data is highly non-linear. It seems obvious to apply the training techniques of neural networks to this type of data. The standard multi-layer perceptron (MLP) and radial basis functions (RBF) were used to model the data; these results were compared to the results found by using a committee of networks. The MLP and RBF architecture was then optimized using particle swarm optimization (PSO). The results from the ´optimal architecture´ networks were then compared to the standard networks and the committee network. We found that, at the expense of computational time, the ´optimal architecture´ RBF and MLP networks achieved better results than both unoptimized networks and the committee of networks.