Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/194686 
Year of Publication: 
2017
Citation: 
[Journal:] Cogent Economics & Finance [ISSN:] 2332-2039 [Volume:] 5 [Issue:] 1 [Publisher:] Taylor & Francis [Place:] Abingdon [Year:] 2017 [Pages:] 1-11
Publisher: 
Taylor & Francis, Abingdon
Abstract: 
This paper conducts a USDTRY rate forecast by ARIMA method using 3,069 daily observations between the dates of 3 January 2005 and 8 March 2017 and generates both long-term and short-term models. Existing works related to USDTRY rate forecast using ARIMA method generate static models, and none of them conduct multi-step prediction or out of sample fit. The work described in this paper, however, applies dynamic model generation and conducts multi-step ahead prediction for out of sample observations. In forecasts performed in this work for USDTRY rate, the short-term ARIMAs outperform the long-term ARIMAs in predicting accuracy. Specifically, for the short-term ARIMAs appropriate specification is raised as ARIMA (2,1,0); on the other hand, for the long-term ARIMAs, the best order is emerged as ARIMA (0,1,1).
Subjects: 
ARIMA
USDTRY
forecast
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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