The estimate of the probability of default plays a central role for any financial entity that wants to have an overview of the risks of insolvency it may incur by having economic relations with counterparties. This study aims to analyze the calculation of such measure in the context of...

Using state-of-the-art recurrent neural network architectures, this study attempts to predict credit default swap risk premia for BR[I]CS countries as accurately as possible. In the time series setting, these recurrent neural networks are ELMAN, NARX, GRU, and LSTM RNNs, considering local and...