Year 2017, Volume 5 , Issue 1, Pages 1 - 14 2017-06-30

Döviz Kuru Getirisinin Tahmininde ELM, ARMA ve ARMA-GARCH Modellerinin Doğruluk Performansının Karşılaştırılması
Comparing Accuracy Performance of ELM, ARMA and ARMA-GARCH Model In Predicting Exchange Rate Return

Nimet Melis Esenyel [1] , Melda Akın [2]


Finansal zaman serilerinin getirilerinin modellenmesinde GARCH tipi modeller ve yapay zeka modelleri sıklıkla kullanılmaktadır. Bu çalışmada ARMA ve ARMA-GARCH modellerinin performansı, yapay zeka tekniklerinden ELM ile karşılaştırılmıştır. Performans karşılaştırmada dört adet hata ölçüm kriterinden yararlanılmıştır. Elde edilen bulgulara göre Euro ve GBP döviz kurlarının ELM modellerinin, ARMA ve ARMA-GARCH modellerine kıyasla daha üstün olduğu görülmüştür. Bu sonuca göre ele alınan dönem içerisinde, döviz kuru getirilerinin tahmininde ELM’ nin daha uygun olduğu söylenebilir
GARCH type models and artificial intelligence models are frequently used in the modeling of financial time series returns. In this study, the performance of ARMA and ARMA-GARCH models was compared with ELM. Four error measurement criteria were used in the performance comparison. According to the findings, ELM models of Euro and GBP exchange rates returns are superior to the ARMA and ARMA-GARCH models. According to this result, it can be said that ELM, one of the artificial intelligence-based methods, is more suitable for estimating the exchange rate returns during the period covered.
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Subjects Social
Journal Section Articles
Authors

Author: Nimet Melis Esenyel
Institution: İSTANBUL ÜNİVERSİTESİ, İKTİSAT FAKÜLTESİ, EKONOMETRİ BÖLÜMÜ
Country: Turkey


Author: Melda Akın
Institution: İSTANBUL ÜNİVERSİTESİ, İKTİSAT FAKÜLTESİ, EKONOMETRİ BÖLÜMÜ
Country: Turkey


Dates

Application Date : March 17, 2017
Acceptance Date : September 17, 2019
Publication Date : June 30, 2017

APA Esenyel, N , Akın, M . (2017). Comparing Accuracy Performance of ELM, ARMA and ARMA-GARCH Model In Predicting Exchange Rate Return. Alphanumeric Journal , 5 (1) , 1-14 . DOI: 10.17093/alphanumeric.298658