Research Article
Open access
Published on 13 September 2023
Download pdf
Zhu,Q. (2023). Forecasting the US Dollar/Euro Exchange Rate Based on ARIMA Model. Advances in Economics, Management and Political Sciences,15,371-380.
Export citation

Forecasting the US Dollar/Euro Exchange Rate Based on ARIMA Model

Qimian Zhu *,1,
  • 1 The High School Affiliated to Renmin University of China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2754-1169/15/20230951

Abstract

The exchange rate is an important indicator for investors and governments as well as a verdict on economic prospects. The euro reached its lowest point in 20 years on August 22, 2022, falling below parity with the dollar and terminating a one-to-one exchange rate with the American currency. The event's great significance provides paramount motivation for us to construct a suitable model capable to forecast it. Numerous modeling methods are proposed to predict and analysis the exchange rate. This paper discussed the applicability of a univariate ARIMA model. Moreover, a multivariable ARIMA model, i.e., four macroeconomic variables supposed influential to the change of exchange rate, were introduced in the AR part of the ARIMA model. For the training set, data is gathered on a monthly basis from January 1999 to December 2021, while the period forecasted is the plunge event from January to August, in 2022. Their 8-month long forecasting experiments reveal that the univariate ARIMA model has no ability to forecast the trend of the exchange rate. In general, the multivariate regression model with ARIMA errors is capable of forecasting the historical change of the USD/EURO spot exchange rate. However, its performance totally depends on the quality of the predicted predictors, which are the four macro-economic variables in this study.

Keywords

USD/Euro exchange rate, ARIMA

[1]. Huang W, Lai K K, Nakamori Y, et al.: Forecasting foreign exchange rates with artificial neural networks: A review. International Journal of Information Technology & Decision Making, 3(01), 145-165(2004).

[2]. Dunis C L, Huang X: Forecasting and trading currency volatility: An application of recurrent neural regression and model combination. Journal of forecasting, 21(5), 317-354, (2002).

[3]. Ghalayini L: Modeling and forecasting the US dollar/euro exchange rate. International Journal of Economics and Finance, 6(1),194-207 (2014).

[4]. Akincilar A, TEMİZ İ, Şahin E: An application of exchange rate forecasting in Turkey. Gazi University Journal of Science, 24(4), 817-828 (2011).

[5]. Kamruzzaman J, Sarker R A. ANN-based forecasting of foreign currency exchange rates. Neural Information Processing-Letters and Reviews, 3(2): 49-58 (2004).

[6]. AsadUllah M, Bashir M A, Aleemi A R: Forecasting Euro against US dollar via combination of NARDL and Univariate techniques during COVID-19. Foresight, (2021).

[7]. Babu A S, Reddy S K: Exchange rate forecasting using ARIMA. Neural Network and Fuzzy Neuron, Journal of Stock & Forex Trading, 4(3), 01-05 (2015).

[8]. Weisang G, Awazu Y: Vagaries of the Euro: an Introduction to ARIMA Modeling. Case Studies In Business, Industry And Government Statistics, 2008, 2(1): 45-55.

[9]. Xu Z. Purchasing power parity, price indices, and exchange rate forcasts. Journal of International Money and Finance, 22(1), 105-130 (2003).

[10]. Hyndman R J, Athanasopoulos G. Forecasting: principles and practice. OTexts, (2018).

Cite this article

Zhu,Q. (2023). Forecasting the US Dollar/Euro Exchange Rate Based on ARIMA Model. Advances in Economics, Management and Political Sciences,15,371-380.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 2nd International Conference on Business and Policy Studies

Conference website: https://2023.confbps.org/
ISBN:978-1-915371-73-7(Print) / 978-1-915371-74-4(Online)
Conference date: 26 February 2023
Editor:Javier Cifuentes-Faura, Canh Thien Dang
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.15
ISSN:2754-1169(Print) / 2754-1177(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).