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

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  • Kutuk, Yasin
  • Barokas, Lina

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.

Suggested Citation

  • Kutuk, Yasin & Barokas, Lina, 2022. "Multivariate CDS risk premium prediction with SOTA RNNs on MI[N]T countries," Finance Research Letters, Elsevier, vol. 45(C).
    • Handle: RePEc:eee:finlet:v:45:y:2022:i:c:s154461232100266x
    DOI: 10.1016/j.frl.2021.102198
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    More about this item

    Keywords

    Credit default swap; Forecasting; Time series; Recurrent neural networks; Deep learning;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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