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

Paulo Canas Rodrigues, Olushina Olawale Awe, Jonatha Sousa Pimentel and Rahim Mahmoudvand
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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
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

Stats, 2020, vol. 3, issue 2, 1-21

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.

Keywords: singular spectrum analysis; multivariate singular spectrum analysis; time series forecasting; artificial neural networks; currency exchange rates (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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