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

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dc.contributor.authorBarokas, Lina
dc.date.accessioned2022-03-30T08:59:50Z
dc.date.available2022-03-30T08:59:50Z
dc.date.issued2022en_US
dc.departmentFakülteler, İktisadi, İdari ve Sosyal Bilimler Fakültesien_US
dc.description.abstractIn 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.en_US
dc.identifier.citationKutuk, Y., Barokas, L. (2022). Multivariate CDS risk premium prediction with SOTA RNNs on MI [N] T countries. Finance Research Letters, 45.en_US
dc.identifier.issn1544-6123
dc.identifier.scopus2-s2.0-85107964009
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2307
dc.identifier.volume45en_US
dc.identifier.wosWOS:000760370100011
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKütük, Yasin
dc.language.isoen
dc.publisherFinance Research Lettersen_US
dc.relation.ispartofFinance Research Letters
dc.relation.isversionof10.1016/j.frl.2021.102198en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCredit Default Swapen_US
dc.subjectForecastingen_US
dc.subjectTime Seriesen_US
dc.subjectRecurrent Neural Networksen_US
dc.subjectDeep Learningen_US
dc.titleMultivariate CDS risk premium prediction with SOTA RNNs on MI[N]T countries
dc.typeArticle

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