31 citations · 152 across the 38 of their papers we have counts for
4 papers · 1 filter
Neural network approximation for superhedging prices
Francesca Biagini, Lukas Gonon, Thomas Reitsam
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 -quan…
Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality
Lukas Gonon
This article investigates the use of random feature neural networks for learning Kolmogorov partial (integro-)differential equations associated to Black-Scholes and more general ex…
Deep ReLU neural networks overcome the curse of dimensionality for partial integrodifferential equations
Lukas Gonon, Christoph Schwab
Deep neural networks (DNNs) with ReLU activation function are proved to be able to express viscosity solutions of linear partial integrodifferental equations (PIDEs) on state space…
Deep ReLU Network Expression Rates for Option Prices in high-dimensional, exponential Lévy models
Lukas Gonon, Christoph Schwab
We study the expression rates of deep neural networks (DNNs for short) for option prices written on baskets of risky assets, whose log-returns are modelled by a multivariate Lé…