Topics in Middle Eastern and North African Economies
Multivariate CDS Risk Premium Prediction with SOTA RNNs on MI[N]T Countries
Document Type
Article
Publication Date
3-1-2022
Abstract
In 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.
Journal Title
Finance Research Letters
Publisher
Elsevier
Volume
45
Recommended Citation
Kutuk, Yasin and Lina Barokas, "Multivariate CDS Risk Premium Prediction with SOTA RNNs on MI[N]T Countries". Topics in Middle Eastern and North African Economies, electronic journal, 45, Elsevier, 2022, http://www.luc.edu/orgs/meea/
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Comments
Presentation of the articles in the Topics in Middle Eastern and North African Economies was made possible by a limited license granted to Loyola University Chicago and Middle East Economics Association from the authors who have retained all copyrights in the articles.