A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options, 14 September 2020

In this paper, we propose a neural network-based method for approximating expected exposures and potential future exposures of Bermudan options. In a first phase, the method relies on the Deep Optimal Stopping algorithm (DOS) proposed in [1], which learns the optimal stopping rule from Monte-Carlo samples of the underlying risk factors.