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

Using dynamic conditional correlations and networks, we bring a novel framework to define the integration and segmentation of emerging countries. The individual EMBI+ spreads of 13 emerging countries from 01/2003 to 12/2013 are used to compare their interaction structure before (phase 1) and after (phase 2) the global financial crisis. Accordingly, the average of dynamic correlations between cross country spreads significantly increases in phase 2. At first, the increased co-movement degree suggests an integration of the sample countries after the crisis. However, correlation based stable networks show that the increase is more likely to be caused by clusters of countries that exhibit high within-cluster co-movement but not between-cluster co-movement. Important implications for international investors and policymakers are discussed. Using dynamic conditional correlations and networks, we bring a novel framework to define the integration and segmentation of emerging countries. The individual EMBI+ spreads of 13 emerging countries from 01/2003 to 12/2013 are used to compare their interaction structure before (phase 1) and after (phase 2) the global financial crisis. Accordingly, the average of dynamic correlations between cross country spreads significantly increases in phase 2. At first, the increased co-movement degree suggests an integration of the sample countries after the crisis. However, correlation based stable networks show that the increase is more likely to be caused by clusters of countries that exhibit high within-cluster co-movement but not between-cluster co-movement. Important implications for international investors and policymakers are discussed.