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Over the past two decades the globalization of market economies have led to a large number of financial crises in emerging markets. The case of Paraguay in earlier '90 of the past century or, more recently, the crises in Turkey, Argentina, and Far East Asian markets have taught the important lesson that such phenomena, originally arising at local basis, can spread contagiously to other markets as well. At the same time, this made clear the importance of Early Warning System (EWS) models to identify economic and financial vulnerabilities among emerging markets, and, ultimately, to anticipate such events. With this in mind, we have introduced an EWS model based on the powerful clustering capabilities of Kohonen's Self Organizing Maps. Using macroeconomic data of several emerging countries, our analysis has been twofold. We have originally provided a static snapshot of countries in our dataset, according to the way their macroeconomic data cluster in the map. In this way, we were able to capture the (eventual) reciprocities and similarities from various emerging markets. As second step, we have dynamically monitored their evolution path in the map over the time. As main results, we were able to develop a crisis indicator to measure the vulnerability of countries, and we have also provided a proper framework to deduce probabilities of future crises.

Early Warning Systems: an approach via Self Organizing Maps with applications to emergent markets