Barokas, Lina2022-03-302022-03-302022Kutuk, Y., Barokas, L. (2022). Multivariate CDS risk premium prediction with SOTA RNNs on MI [N] T countries. Finance Research Letters, 45.1544-6123https://hdl.handle.net/20.500.12939/2307In this study, CDS risk premiums of Mexico, Indonesia and Turkey were predicted by applying state-of-the-art forecasters in deep learning recurrent neural networks architectures which are the most recent ground-breaking predictors in the time series setting. The predictive power of each sota forecaster is compared, and the results are differentiated by country and type of sota predictors. While the long short-term memory model is better to predict Mexico’s CDS risk premiums, the nonlinear autoregressive network with exogenous inputs model is found to be more suitable for Indonesia and Turkey. The results of Turkey model reached the highest forecast accuracy.eninfo:eu-repo/semantics/closedAccessCredit Default SwapForecastingTime SeriesRecurrent Neural NetworksDeep LearningArticle452-s2.0-85107964009Q1WOS:000760370100011Q1