Option pricing using deep learning approach based on LSTM-GRU neural networks: Case of London stock exchange

Abstract

This study is a review of literature on machine learning to examine the potential of deep learning (DL) techniques in improving the accuracy of option pricing models versus the Black-Scholes model and capturingcomplex features in financial data. Neural networks and other machine learning models have been proposed for option pricing and have improved accuracy compared withtraditional models. However, such use of machine learning also presents practical challenges such as data availability and quality, computational resources, model selection and validation, interpretability and overfitting. This study discusses several of these challenges and highlights the need for careful evaluation and validation of machine learning models in London option pricing during the Coronavirus disease 2019 pandemic. Moreover, to investigate the quality of the models used, we compare the performances of these algorithms in option pricing through the application of significance statistical tests.

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