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Opening the black box – Quantile neural networks for loss given default prediction

Kellner, Ralf ; Nagl, Maximilian ; Rösch, Daniel


We extend the linear quantile regression with a neural network structure to enable more flexibility in every quantile of the bank loan loss given default distribution. This allows us to model interactions and non-linear impacts of any kind without the need of specifying the exact form beforehand. The precision of the quantile forecasts increases up to 30% compared to the benchmark, especially for ...


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