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.
https://wsc.project.cwi.nl/abcxva/overview-research-programme/publications/esr2-kristoffer-andersson/2003-01977.pdf/view
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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.