Mapping the Stocks in MICEX: Who Is Central to the Moscow Stock Exchange?

Abstract

In this article we use partial correlations to derive bidirectional connections between major …rms listed in the Moscow Stock Exchange. We obtain coe¢ cients of partial correlation from the correlation estimates of the Constant Conditional Correlation GARCH (CCC-GARCH) and the consistent Dynamic Conditional Correlation GARCH (cDCC-GARCH) models. We map the graph of partial correlations using the Gaussian Graphical Model and apply network analysis to identify the most central …rms in terms of both shock propagation and connectedness with others. Moreover, we analyze some network characteristics over time and based on these we construct a measure of system vulnerability to external shocks. Our …ndings suggest that during the crisis interconnectedness between …rms strengthens and becomes polarized and the system becomes more vulnerable to systemic shocks. In addition, we found that the most connected …rms are the state-owned …rms Sberbank and Gazprom and the private oil company Lukoil, while in the top most central in terms of systemic risk contributors Sberbank gave its place to NLMK Group.