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- W4312178575 abstract "Abstract We consider the computation by simulation and neural net regression of conditional expectations, or more general elicitable statistics, of functionals of processes . Here an exogenous component Y (Markov by itself) is time‐consuming to simulate, while the endogenous component X (jointly Markov with Y ) is quick to simulate given Y , but is responsible for most of the variance of the simulated payoff. To address the related variance issue, we introduce a conditionally independent, hierarchical simulation scheme, where several paths of X are simulated for each simulated path of Y . We analyze the statistical convergence of the regression learning scheme based on such block‐dependent data. We derive heuristics on the number of paths of Y and, for each of them, of X , that should be simulated. The resulting algorithm is implemented on a graphics processing unit (GPU) combining Python/CUDA and learning with PyTorch. A CVA case study with a nested Monte Carlo benchmark shows that the hierarchical simulation technique is key to the success of the learning approach." @default.
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- W4312178575 date "2022-12-23" @default.
- W4312178575 modified "2023-10-17" @default.
- W4312178575 title "Pathwise CVA regressions with oversimulated defaults" @default.
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- W4312178575 doi "https://doi.org/10.1111/mafi.12368" @default.
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