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Mapping The Stocks In Micex: Who Is Central To The Moscow Stock Exchange?

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  • M. Hakan Eratalay; Evgenii V. Vladimirov

Abstract

In this article we use partial correlations to derive bidirectional 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 the top most central in terms of systemic risk contributors Sberbank gave its place to NLMK Group.

Suggested Citation

  • M. Hakan Eratalay; Evgenii V. Vladimirov, 2018. "Mapping The Stocks In Micex: Who Is Central To The Moscow Stock Exchange?," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 111, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    • Handle: RePEc:mtk:febawb:111
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    Cited by:

    1. Ariana Paola Cortés Ángel & Mustafa Hakan Eratalay, 2022. "Deep diving into the S&P Europe 350 index network and its reaction to COVID-19," Journal of Computational Social Science, Springer, vol. 5(2), pages 1343-1408, November.
    2. Mustafa Hakan Eratalay & Ariana Paola Cortés Ángel, 2022. "The Impact of ESG Ratings on the Systemic Risk of European Blue-Chip Firms," JRFM, MDPI, vol. 15(4), pages 1-41, March.
    3. Ariana Paola Cortés à ngel & Mustafa Hakan Eratalay, 2021. "Deedp Diving Into The S&P 350 Europe Index Network Ans Its Reaction To Covid-19," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 134, Faculty of Economics and Business Administration, University of Tartu (Estonia).

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    More about this item

    Keywords

    Multivariate GARCH; Volatility Spillovers; Network connections; MICEX;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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