Multivariate CDS risk premium prediction with SOTA RNNs on MI[N]T countries
[ X ]
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Finance Research Letters
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Credit Default Swap, Forecasting, Time Series, Recurrent Neural Networks, Deep Learning
Kaynak
Finance Research Letters
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
45
Sayı
Künye
Kutuk, Y., Barokas, L. (2022). Multivariate CDS risk premium prediction with SOTA RNNs on MI [N] T countries. Finance Research Letters, 45.
