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Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks

Author

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  • Paulo Canas Rodrigues

    (Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil)

  • Olushina Olawale Awe

    (Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil
    Department of Mathematical Sciences, Anchor University Lagos, Lagos 100278, Nigeria)

  • Jonatha Sousa Pimentel

    (Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil)

  • Rahim Mahmoudvand

    (Department of Statistics, Bu-Ali Sina University, Hamedan 6517833131, Iran)

Abstract

A proper understanding and analysis of suitable models involved in forecasting currency exchange rates dynamics is essential to provide reliable information about the economy. This paper deals with model fit and model forecasting of eight time series of historical data about currency exchange rate considering the United States dollar as reference. The time series techniques: classical autoregressive integrated moving average model, the non-parametric univariate and multivariate singular spectrum analysis (SSA), artificial neural network (ANN) algorithms, and a recent prominent hybrid method that combines SSA and ANN, are considered and their performance compared in terms of model fit and model forecasting. Moreover, specific methodological and computational adaptations were conducted to allow for these analyses and comparisons.

Suggested Citation

  • Paulo Canas Rodrigues & Olushina Olawale Awe & Jonatha Sousa Pimentel & Rahim Mahmoudvand, 2020. "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks," Stats, MDPI, vol. 3(2), pages 1-21, June.
    • Handle: RePEc:gam:jstats:v:3:y:2020:i:2:p:12-157:d:366020
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    References listed on IDEAS

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    1. Golyandina, Nina & Korobeynikov, Anton & Shlemov, Alex & Usevich, Konstantin, 2015. "Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i02).
    2. Sulandari, Winita & Subanar, & Lee, Muhammad Hisyam & Rodrigues, Paulo Canas, 2020. "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks," Energy, Elsevier, vol. 190(C).
    3. Ca’ Zorzi, Michele & Kolasa, Marcin & Rubaszek, Michał, 2017. "Exchange rate forecasting with DSGE models," Journal of International Economics, Elsevier, vol. 107(C), pages 127-146.
    4. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    5. Adam Stokes & Ahmed S. Abou-Zaid, 2012. "Forecasting foreign exchange rates using artificial neural networks: a trader's approach," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 5(4), pages 370-394.
    6. Hassani, Hossein & Heravi, Saeed & Zhigljavsky, Anatoly, 2009. "Forecasting European industrial production with singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 25(1), pages 103-118.
    7. Jordi Galí & Tommaso Monacelli, 2005. "Monetary Policy and Exchange Rate Volatility in a Small Open Economy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 707-734.
    8. Rahim Mahmoudvand & Dimitrios Konstantinides & Paulo Canas Rodrigues, 2017. "Forecasting mortality rate by multivariate singular spectrum analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(6), pages 717-732, November.
    9. de Carvalho, Miguel & Rodrigues, Paulo C. & Rua, António, 2012. "Tracking the US business cycle with a singular spectrum analysis," Economics Letters, Elsevier, vol. 114(1), pages 32-35.
    10. Sebastian Edwards & Miguel A. Savastano, 1999. "Exchange Rates in Emerging Economies: What Do We Know? What Do We Need to Know?," NBER Working Papers 7228, National Bureau of Economic Research, Inc.
    11. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
    12. Nag, Ashok K & Mitra, Amit, 2002. "Forecasting Daily Foreign Exchange Rates Using Genetically Optimized Neural Networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(7), pages 501-511, November.
    13. Rahim Mahmoudvand & Paulo Canas Rodrigues, 2018. "A new parsimonious recurrent forecasting model in singular spectrum analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(2), pages 191-200, March.
    14. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    15. von Hagen, Jurgen & Zhou, Jizhong, 2007. "The choice of exchange rate regimes in developing countries: A multinomial panel analysis," Journal of International Money and Finance, Elsevier, vol. 26(7), pages 1071-1094, November.
    16. Paul Alagidede & Muazu Ibrahim, 2017. "On the Causes and Effects of Exchange Rate Volatility on Economic Growth: Evidence from Ghana," Journal of African Business, Taylor & Francis Journals, vol. 18(2), pages 169-193, April.
    17. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2018. "The M4 Competition: Results, findings, conclusion and way forward," International Journal of Forecasting, Elsevier, vol. 34(4), pages 802-808.
    18. Hassani, Hossein, 2007. "Singular Spectrum Analysis: Methodology and Comparison," MPRA Paper 4991, University Library of Munich, Germany.
    19. Andreas Groth & Michael Ghil, 2017. "Synchronization of world economic activity," Post-Print hal-01701086, HAL.
    20. Montero, Pablo & Vilar, José A., 2014. "TSclust: An R Package for Time Series Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i01).
    21. Olushina Olawale Awe & Luis Alberiko Gil-Alana, 2019. "Time series analysis of economic growth rate series in Nigeria: structural breaks, non-linearities and reasons behind the recent recession," Applied Economics, Taylor & Francis Journals, vol. 51(50), pages 5482-5489, October.
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    2. Juan D. Borrero & Jesús Mariscal & Alfonso Vargas-Sánchez, 2022. "A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors," Stats, MDPI, vol. 5(4), pages 1-14, November.
    3. Awe, Olushina Olawale & Dias, Ronaldo, 2022. "Comparative Analysis of ARIMA and Artificial Neural Network Techniques for Forecasting Non-Stationary Agricultural Output Time Series," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 14(4), December.

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