Quantitative comparison of option pricing models: neural networks vs. stochastic models - Nyholm, Sebastian (2024)
This study presents a quantitative comparison of three different option pricing models. The emphasis is on the quite recent artificial neural network model, which is compared to the Monte Carlo simulation and the Black-Scholes-Merton pricing model. The financial markets' complexity demands increasingly sophisticated models, and recent advances in computing power have facilitated the development of intricate option pricing models. Especially the sub-model derived from artificial neural networks, the multilayer perceptron has been used in pricing European call options. Existing literature demonstrates the multilayer perceptron's superiority in pricing accuracy compared to the Black-Scholes-Merton model. However, successful implementation necessitates specific model inputs, defined network architecture, and a substantial amount of data. The results of this study underscore the better predictive accuracy of the artificial neural networks when compared to the stochastic models, as it is more accurate in predicting the option prices when using the complete testing dataset. Notably, the artificial neural network exhibits exceptional performance when pricing out-of-the-money options, with diminishing discrepancies to the stochastic models observed with in-the-money options, to the point of the network’s results being comparable to the results of the stochastic models. The two stochastic models used in this thesis expectedly perform extremely similarly. The optimal network architecture identified diverges notably from those architectures used in prior literature, featuring significantly greater numbers of hidden layers and neurons per layer. However, despite the large network size this does not cause overfitting problems, and this is somewhat attributable to the large reliable dataset. The time period used, along with the chronological data partitioning method, caused problems, ultimately leading to the decision to drop the interest rate variable from the network model altogether.