Neural network approximation for superhedging prices

Understand

This article examines neural network-based approximations for the superhedging price process of a contingent claim in a discrete time market model.

• First we prove that the $\alpha$-quantile hedging price converges to the superhedging price at time $0$ for $\alpha$ tending to $1$, and show that the $\alpha$-quantile hedging price can be approximated by a neural network-based price.
• This provides a neural network-based approximation for the superhedging price at time $0$ and also the superhedging strategy up to maturity.
• To obtain the superhedging price process for $t>0$, by using the Doob decomposition it is sufficient to determine the process of consumption.