Modelling option prices using neural networks
Abstract
In this research, modelling of the European option prices of S&P 500 index options
was carried out using Multi-layer Perceptron Neural Networks. The goal was to
train the neural networks using historical data to accurately determine option prices,
given the index price, strike price and time to expiry as inputs. There is no hard
and fast formula for pricing options, with the exception of the Black Scholes model,
which is only a theoretical model and often under-performs in practical applications.
Therefore, developing a model for pricing real options is of great importance, and
Neural Networks have the potential to be vital vehicles to that end. That is what
motivated this study. Di erent results with respect to accuracy are achieved by
partitioning the data according to moneyness of options, with the Neural Network
performing exceptionally for in-the-money options, but poorly for out-of-the-money
options. This suggest that in a volatile market the neural network outperforms the
Black Scholes model for in-the-money options, however the Black Scholes model is
still better for at-the-money options.