Analyzing of Systemic Risk Contributions of Tehran Stock Exchange Companies by Complexity Approach

Abstract

With the recent extension of markets and increasing financial interactions, institutions are affected by their systemic risk and the systemic risk of other institutions and markets. Also, by changing the structure and characteristics of institutions in their complex network, the Systemic risk contribution of these institutions will be different. It is important for lawmakers, investors, and others to control, manage and reduce systemic relationship between local topology structure and systemic risk contribution by panel data regression analysis, it found that there is a significant relationship between the change of Conditional Value-at-Risk (∆CoVaR) and the local topology structure such as node closeness centrality, node strength, and node degree. So, there is a significant relationship between systemic risk contribution and the local topology structure. The results show that there is a positive relationship between systemic risk contribution and node closeness centrality, so financial institutions with larger node closeness centrality have higher systemic risk contributions. Also, there is a negative relationship between systemic risk contribution and node strength and node degree. Therefore, financial institutions with greater node strength and larger node degrees have lower systemic risk contributions. But with the data analyzed in this study, no significant relationship is found between node betweenness centrality and systemic risk contributions.risk. The purpose of this paper is to analyze the structure of the financial institutions' local topology on their systemic risk contribution. The purpose of this study is to investigate the contribution of systemic risk, using Tehran stock exchange data (on twenty stock companies from March 2014 to March 2019) with the change of Conditional Value-at-Risk (∆CoVaR). Initially, a dynamic conditional correlation multivariate GARCH model (DCC-MVGARCH) is used to calculate the conditional correlation matrix and the minimum spanning tree (MST) is constructed. Then, the topology structure of the financial institutions' network and relationships between these characteristics and systemic risk is estimated. By quantifying the

Keywords

• systemic risk contribution
• dcc
• complex networks
• minimum spanning tree