Improved Model-Free Bounds for Multi-Asset Options Using Option-Implied Information and Deep Learning

The authors provide a fundamental theorem of asset pricing and a superhedging duality in a setting that combines dependence uncertainty with additional information on the dependence structure in the form of known prices for multi-asset options. They solve the resulting optimization problem using a penalization approach combined with a deep learning approximation.