CDS risk premia forecasting with multi-featured deep RNNs: An application on BR[I]CS countries
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Borsa Istanbul Anonim Sirketi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Using state-of-the-art recurrent neural network architectures, this study attempts to predict credit default swap risk premia for BR[I]CS countries as accurately as possible. In the time series setting, these recurrent neural networks are ELMAN, NARX, GRU, and LSTM RNNs, considering local and global features. The predictive power of each architecture is compared, and the results differ depending on the country. NARX RNN was the best predictor for Brazil and South Africa in various settings. Meanwhile, ELMAN RNN produces more accurate results in China, whereas Russia's long short-term memory RNN achieves the best predictors among other countries’ RNNs.
Açıklama
Anahtar Kelimeler
Credit default swap premium, Deep learning, Prediction, Recurrent neural networks, Time series
Kaynak
Borsa Istanbul Review
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
Sayı
Künye
Kütük, Yasin. (2023). CDS risk premia forecasting with multi-featured deep RNNs: An application on BR[I]CS countries. Borsa Istanbul Review.
