Pricing European Options with Google AutoML, TensorFlow, and XGBoost
It addresses the problem of pricing European options for finance practitioners, but it is incremental as it applies existing methods to a known task.
This paper compared Google AutoML, TensorFlow neural networks, and XGBoost for pricing European options, finding that all three machine learning models outperformed the Black-Scholes model in terms of mean absolute error.
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.