Multivariate CDS risk premium prediction with SOTA RNNs on MI[N]T countries

Yasin Kutuk and Lina Barokas

Finance Research Letters, 2022, vol. 45, issue C

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.

Keywords: Credit default swap; Forecasting; Time series; Recurrent neural networks; Deep learning (search for similar items in EconPapers)
JEL-codes: C45 C52 C53 E37 E66 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:45:y:2022:i:c:s154461232100266x

DOI: 10.1016/j.frl.2021.102198

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