Option Pricing using Artificial Neural Networks

Abstract: Neural networks have an increasingly important role in the financial market, by offering a solution to stationarity and non-linearity whilst also providing robustness and predictive power. Options and option pricing are a fundamental area of interest in the daily activities of investment banks, hedge funds and trading firms in the financial market. Implied volatility is the focal point of these operations and an intricate and essential parameter to be taken under consideration as it provides the user a numerical estimation of risk and provides the basis for modeling and risk management. There are a number of numerical root solving algorithms which form the basis of determining the implied volatility from a given data set. However, these algorithms bring an inherent trade-off between convergence, robustness and computational efficiency. An artificial neural network approach to determining implied volatility aims to address these issues and provide the most suitable, stable and computationally efficient method. In addition, due to the extended complexity of the network, it is possible to determine the most popular options related metrics denoted as -- The Greeks -- from the weights of the trained network. These metrics provide the investor with the sensitivity of the respective option prices to the underlying parameters present in the market. This is of particular importance in models where a closed form solution is not available. Furthermore, this paper will attempt to generalize the option framework, providing opportunity to encompass both European and American options while also giving rise to further extensions into the exotic option modeling process.