Mapping the stocks in MICEX: Who is central in the Moscow Stock Exchange?

Abstract

In this article, we use partial correlations to derive bi‐directional connections between major firms listed in the Moscow Stock Exchange. We obtain coefficients 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 firms 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 findings suggest that during the crisis interconnectedness between firms strengthens and becomes polarized and the system becomes more vulnerable to systemic shocks. In addition, we found that the most connected firms are the state‐owned firms Sberbank and Gazprom and the private oil company Lukoil, while in terms of the top most central systemic risk contributors, Sberbank gave its place to the NLMK Group.

Deep diving into the S&P Europe 350 index network and its reaction to COVID-19

“…The econometric analysis will be based mainly on the work of Eratalay and Vladimirov [ 24 ]. Instead of the unobservable factor in their model, we consider the Morgan Stanley Capital International World (MSCI World) index as a common observable factor.…”

“…5 We include the common observable factor, which otherwise would bring about spurious connections in the network. (See a discussion in Barigozzi and Brownlees, [ 9 ] and Eratalay and Vladimirov, [ 24 ]). We chose MSCI World as an indicator of the general trend in the behaviour of developed economies worldwide.…”

“…The estimation for the conditional mean, conditional variance and conditional correlation parameters is realised using the three-step estimation following the Eratalay and Vladimirov [ 24 ] path. The resulting quasi-maximum likelihood estimators are consistent and asymptotically normal.…”

“…We derive this relationship from the partial correlation matrix. This method has been widely applied and modified; to mention some Eratalay and Vladimirov [ 24 ], Kenett et al [ 39 ], Anufriev and Panchenko [ 8 ] and Iori and Mantegna [ 35 ] write a compendium of several studies and their different applications, some of them using this same approach, all with the idea of understanding how a network reacts to disruption in greater depth.…”

“…We use the consistent dynamic conditional correlation model (cDCC-GARCH), and the multivariate model presented by Aielli [ 2 ]. Following the same theoretical approach as in Eratalay and Vladimirov [ 24 ] and Anufriev and Panchenko [ 8 ], we obtain the partial correlation 1 network by applying the Gaussian Graphical Model algorithm (GGM). Then we obtain global and local measurements of the network to identify which companies are most sensitive to external changes given the system’s structure.…”