Not all emerging markets are the same: A classification approach with correlation based networks

Using dynamic conditional correlations and network theory, this study brings a novel interdisciplinary framework to define the integration and segmentation of emerging countries. The individual EMBI+ spreads of 13 emerging countries from January 2003 to December 2013 are used to compare their interaction structure before (phase 1) and after (phase 2) the global financial crisis. Accordingly, the unweighted average of dynamic conditional correlations between cross country bond returns significantly increases in phase 2. At first glance, the increased co-movement degree suggests an integration of the sample countries after the crisis. However, using correlation based stable networks, we show that this is not enough to make such a strong conclusion. In particular, we reveal that the increased average correlation is more likely to be caused by clusters of countries that exhibit high within-cluster co-movement but not between-cluster co-movement. Potential reasons for the post-crisis segmentation and important implications for international investors and policymakers are discussed.