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Grey theory to predict Ethiopian foreign currency exchange rate

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  • Natnael Nigussie Goshu
  • Surafel Luleseged Tilahun

Abstract

A system containing known values and uncertain unknown values is called a Grey system. Grey system requires only a limited amount of data to estimate the behaviour of unknown systems with poor, incomplete or uncertain information. In this paper, the accuracies of different Grey system models such as GM(1,1), FRMGM(1,1), VGM and FRMVGM are investigated. In addition to this, Linear Regression model is also used for comparison. These Grey models solve complex and sophisticated problems like foreign currency exchange. Foreign currency exchange rates are affected by many highly correlated factors. These factors could be economic, political and even psychological factors, and each of them affect the rate of currency exchange in difference level from time to time. Foreign currency exchange rate from Commercial Bank of Ethiopia between November 2014 and October 2015 are used to compare the performance of different models. The simulation result shows that FRMGM(1,1) is the best in model fitting and forecasting foreign currency exchange.

Suggested Citation

  • Natnael Nigussie Goshu & Surafel Luleseged Tilahun, 2016. "Grey theory to predict Ethiopian foreign currency exchange rate," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 2(2), pages 95-116.
    • Handle: RePEc:ids:ijbfmi:v:2:y:2016:i:2:p:95-116
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    References listed on IDEAS

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    1. Huang, G. H. & Baetz, B. W. & Patry, G. G., 1997. "A response to "A comment on 'Grey integer programming: An application to waste management planning under uncertainty"' by Larry Jenkins," European Journal of Operational Research, Elsevier, vol. 100(3), pages 638-641, August.
    2. Huang, G. H. & Baetz, B. W. & Patry, G. G., 1995. "Grey fuzzy integer programming: An application to regional waste management planning under uncertainty," Socio-Economic Planning Sciences, Elsevier, vol. 29(1), pages 17-38, March.
    3. Tang, Hui-Wen Vivian & Yin, Mu-Shang, 2012. "Forecasting performance of grey prediction for education expenditure and school enrollment," Economics of Education Review, Elsevier, vol. 31(4), pages 452-462.
    4. Huafeng Xu & Bin Liu & Zhigeng Fang, 2014. "New grey prediction model and its application in forecasting land subsidence in coal mine," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(2), pages 1181-1194, March.
    5. Muntean, Mihaela, 2012. "Business Intelligence Approaches," MPRA Paper 41139, University Library of Munich, Germany, revised 03 Jun 2012.
    6. Huang, Guo H. & Baetz, Brian W. & Patry, Gilles G., 1995. "Grey integer programming: An application to waste management planning under uncertainty," European Journal of Operational Research, Elsevier, vol. 83(3), pages 594-620, June.
    7. Chang, Tsung-Sheng & Ku, Cheng-Yuan & Fu, Hsin-Pin, 2013. "Grey theory analysis of online population and online game industry revenue in Taiwan," Technological Forecasting and Social Change, Elsevier, vol. 80(1), pages 175-185.
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