Pricing European Options with Google AutoML, TensorFlow, and XGBoost
Juan Esteban Berger
Researchers have been using Neural Networks and other related
machine-learning techniques to price options since the early 1990s. After three
decades of improvements in machine learning techniques, computational
processing power, cloud computing, and data availability, this paper is able to
provide a comparison of using Google Cloud's AutoML Regressor, TensorFlow
Neural Networks, and XGBoost Gradient Boosting Decision Trees for pricing
European Options. All three types of models were able to outperform the Black
Scholes Model in terms of mean absolute error. These results showcase the
potential of using historical data from an option's underlying asset for
pricing European options, especially when using machine learning algorithms
that learn complex patterns that traditional parametric models do not take into
account.