1 citations · 1 across the 1 of their papers we have counts for
6 papers
Quasi-Monte Carlo with Domain Transformation for Efficient Fourier Pricing of Multi-Asset Options
Christian Bayer, Chiheb Ben Hammouda, Antonis Papapantoleon +2
Efficiently pricing multi-asset options poses a significant challenge in quantitative finance. Fourier methods leverage the regularity properties of the integrand in the Fourier do…
Convergence of the generalization error for deep gradient flow methods for PDEs
Chenguang Liu, Antonis Papapantoleon, Jasper Rou
The aim of this article is to provide a firm mathematical foundation for the application of deep gradient flow methods (DGFMs) for the solution of (high-dimensional) partial differ…
Machine learning for option pricing: an empirical investigation of network architectures
Serena Della Corte, Laurens Van Mieghem, Antonis Papapantoleon +1
We consider the supervised learning problem of learning the price of an option or the implied volatility given appropriate input data (model parameters) and corresponding output da…
Stability of backward propagation of chaos
Antonis Papapantoleon, Alexandros Saplaouras, Stefanos Theodorakopoulos
The purpose of the present paper is to introduce and establish a notion of stability for the backward propagation of chaos with respect to (initial) data sets. Consider, for exampl…
A time-stepping deep gradient flow method for option pricing in (rough) diffusion models
Antonis Papapantoleon, Jasper Rou
We develop a novel deep learning approach for pricing European options in diffusion models, that can efficiently handle high-dimensional problems resulting from Markovian approxima…
A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models
Emmanuil H. Georgoulis, Antonis Papapantoleon, Costas Smaragdakis
We develop a novel deep learning approach for pricing European basket options written on assets that follow jump-diffusion dynamics. The option pricing problem is formulated as a p…