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A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively?

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Author Info
Lin, Chin-Shien
Khan, Haider A.
Chang, Ruei-Yuan
Wang, Ying-Chieh

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Abstract

This paper presents a hybrid causal model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of the neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to neural network and the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach based on determinate causal mechanisms towards finding ways to prevent currency crises.

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Publisher Info
Article provided by Elsevier in its journal Journal of International Money and Finance.

Volume (Year): 27 (2008)
Issue (Month): 7 (November)
Pages: 1098-1121
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Handle: RePEc:eee:jimfin:v:27:y:2008:i:7:p:1098-1121

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Web page: http://www.elsevier.com/locate/inca/30443

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Related research
Keywords: Currency crises Neuro fuzzy Inductive learning Signal approach Logit;

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  20. Kaminsky, G.L. & Leiderman, L., 1996. "High Real Interest Rates in the Aftermath of Disinflation: Is It a Lack of Credibility," Papers 8-96, Tel Aviv.
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  22. Inci Ötker & Ceyla Pazarbasioglu, 1994. "Exchange Market Pressures and Speculative Capital Flows in Selected European Countries," IMF Working Papers 94/21, International Monetary Fund.
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  26. Velasco, Andres, 1987. "Financial crises and balance of payments crises : A simple model of the southern cone experience," Journal of Development Economics, Elsevier, vol. 27(1-2), pages 263-283, October. [Downloadable!] (restricted)
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