Comprehensive risk assessment of financial institutions in the Tehran Stock Exchange using centrality metrics and dynamic clustering based on the Markov process

Systemic risk is the risk imposed by a financial institution on the entire economy, the importance of which has become clear to many policymakers and economists since the financial crisis of 2008, and its measurement has been put on the agenda of many researchers. The present study presents a combined method of systemic risk measurement, in which the shape of the communication graph and the structural characteristics of financial institutions are simultaneously considered. In the proposed method, first, the communication graph is clustered using the Markov clustering algorithm. Then the systemic risk of each financial institution is measured according to its position in the cluster and using the adjusted semi-local centrality systemic risk measure.  The effectiveness of the proposed method has been investigated for banks registered with the Tehran Stock Exchange and Securities Organization from 2014 to 2018 with monthly periods. Based on the results, the linear correlation of systemic risk changes calculated based on the proposed method with systemic risk calculated through simulation (SIR) was higher than the correlation of systemic risk calculated with $\Delta$CoVaR and PageRank measures. Also, based on the results, Mellat, Trade and Export banks have the highest systemic risk and the lowest systemic risk related to capital, and tourism banks.