Abstract:We introduce a new portfolio credit risk model based on Restricted Boltzmann Machines (RBMs), which are stochastic neural networks capable of universal approximation of loss distributions.We test the model on an empirical dataset of default probabilities of 30 investmentgrade US companies and we show that it outperforms commonly used parametric factor copula models -such as the Gaussian or the t factor copula models -across several credit risk management tasks. In particular, the model leads to better out-of-s… Show more
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