Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies
Files
(Published version)
Date
2016
Authors
Allen, D.E.
McAleer, M.
Peiris, S.
Singh, A.K.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Risks, 2016; 4(1):1-14
Statement of Responsibility
Conference Name
Abstract
This paper features an analysis of major currency exchange rate movements in relation tothe US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japaneseyen are modelled using a variety of non-linear models, including smooth transition regressionmodels, logistic smooth transition regressions models, threshold autoregressive models, nonlinearautoregressive models, and additive nonlinear autoregressive models, plus Neural Networkmodels. The models are evaluated on the basis of error metrics for twenty day out-of-sampleforecasts using the mean average percentage errors (MAPE). The results suggest that there is nodominating class of time series models, and the different currency pairs relationships with the USdollar are captured best by neural net regression models, over the ten year sample of daily exchangerate returns data, from August 2005 to August 2015.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an openaccess article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/)