Gated Neural Networks for Option Pricing: Rationality by Design
This work addresses option pricing for financial practitioners by providing a data-driven method with built-in economic rationality, though it appears incremental as it builds on neural network approaches with specific constraints.
The authors tackled the problem of pricing European call options by developing gated neural networks that automatically learn to divide-and-conquer the problem space, resulting in significantly better generalization and performance compared to existing models while guaranteeing economically reasonable predictions.
We propose a neural network approach to price EU call options that significantly outperforms some existing pricing models and comes with guarantees that its predictions are economically reasonable. To achieve this, we introduce a class of gated neural networks that automatically learn to divide-and-conquer the problem space for robust and accurate pricing. We then derive instantiations of these networks that are 'rational by design' in terms of naturally encoding a valid call option surface that enforces no arbitrage principles. This integration of human insight within data-driven learning provides significantly better generalisation in pricing performance due to the encoded inductive bias in the learning, guarantees sanity in the model's predictions, and provides econometrically useful byproduct such as risk neutral density.