CDS risk premia forecasting with multi-featured deep RNNs: An application on BR[I]CS countries

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dc.contributor.authorKütük, Yasin
dc.date.accessioned2023-11-16T08:38:21Z
dc.date.available2023-11-16T08:38:21Z
dc.date.issued2023en_US
dc.departmentFakülteler, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Ekonomi Bölümüen_US
dc.description.abstractUsing 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.en_US
dc.identifier.citationKütük, Yasin. (2023). CDS risk premia forecasting with multi-featured deep RNNs: An application on BR[I]CS countries. Borsa Istanbul Review.en_US
dc.identifier.issn2214-8450
dc.identifier.scopus2-s2.0-85175864895
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4238
dc.identifier.wosWOS:001140030000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKütük, Yasin
dc.language.isoen
dc.publisherBorsa Istanbul Anonim Sirketien_US
dc.relation.ispartofBorsa Istanbul Review
dc.relation.isversionof10.1016/j.bir.2023.10.013en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCredit default swap premiumen_US
dc.subjectDeep learningen_US
dc.subjectPredictionen_US
dc.subjectRecurrent neural networksen_US
dc.subjectTime seriesen_US
dc.titleCDS risk premia forecasting with multi-featured deep RNNs: An application on BR[I]CS countries
dc.typeArticle

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