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

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.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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