Early‐warning analysis for currency crises in emerging markets: A revisit with fuzzy clustering

Abstract

AbstractCurrency crises, also often called balance‐of‐payment crises, occur when massive capital outflows force a country to devalue or float its currency. The world‐wide integration of capital markets since the 1980s and 1990s has increased the degree of capital mobility, which also determined a substantial turbulence in foreign exchange markets and frequent currency crises. In this paper, we explore advanced supporting instruments for predicting currency crises, based on an empirical study of the currency crisis episodes in 23 emerging markets around the world during the second half of last century. More specifically, we investigate the usefulness of prediction models built based on the fuzzy c‐means method. First we build clustering models that partition data into a certain number of overlapping natural groups. Thereafter, we classify the data clusters into early‐warning clusters and tranquil clusters. We compare the performance of our models with a conventional c‐means clustering model and a benchmark probit model. The results show that the proposed models achieve a similar level of out‐of‐sample performance as the probit model and c‐means model. The fuzzy approach also introduces additional explanatory advantages into the early‐warning analysis process. Copyright © 2010 John Wiley & Sons, Ltd.