A KELM-Based Ensemble Learning Approach for Exchange Rate Forecasting

Wei Yunjie (), Sun Shaolong (), Lai Kin Keung () and Abbas Ghulam ()
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Wei Yunjie: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
Sun Shaolong: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
Lai Kin Keung: International Business School, Shaanxi Normal University, Xi‘an, 710119, China
Abbas Ghulam: School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China

Journal of Systems Science and Information, 2018, vol. 6, issue 4, 289-301

Abstract: In this paper, a KELM-based ensemble learning approach, integrating Granger causality test, grey relational analysis and KELM (Kernel Extreme Learning Machine), is proposed for the exchange rate forecasting. The study uses a set of sixteen macroeconomic variables including, import, export, foreign exchange reserves, etc. Furthermore, the selected variables are ranked and then three of them, which have the highest degrees of relevance with the exchange rate, are filtered out by Granger causality test and the grey relational analysis, to represent the domestic situation. Then, based on the domestic situation, KELM is utilized for medium-term RMB/USD forecasting. The empirical results show that the proposed KELM-based ensemble learning approach outperforms all other benchmark models in different forecasting horizons, which implies that the KELM-based ensemble learning approach is a powerful learning approach for exchange rates forecasting.

Keywords: exchange rate; macroeconomic variables; forecasting; kernel extreme learning machine (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:6:y:2018:i:4:p:289-301:n:1

DOI: 10.21078/JSSI-2018-289-13

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