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

Document Type : Research Paper


1 Department of Financial Engineering, Qom Branch, Islamic Azad University, Qom, Iran

2 Department of Finance, Central Tehran Branch, Islamic Azad University, Tehran, Iran

3 Department of Finance, Eslamshahr Branch, Islamic Azad University, Tehran, Iran

4 Department of Accounting, Qom Branch, Islamic Azad University, Qom, Iran



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.


[1] T. Adrian and M.K. Brunnermeier, CoVaR (No. w17454), National Bureau of Economic Research, 2011.
[2] A.M. Andries and E. Galasan, Measuring financial contagion and spillover effects with a state-dependent sensitivity value-at-risk model, Risks 8 (2020), no. 1, 5.
[3] S. Benoit, G. Colletaz, C. Hurlin, and C. Perignon, A theoretical and empirical comparison of systemic risk measures, HEC Paris Research Paper No.1030 FIN(204), Available at SSRN:, (2013).
[4] M. Bhattacharya, J.N. Inekwe, and M.R. Valenzuela, Credit risk and financial integration: An application of network analysis, Int. Rev. Financ. Ana. 72 (2020), 101588.
[5] D. Bianchi, M. Billio, R. Casarin, and M. Guidolin, Modeling systemic risk with Markov switching graphical SUR models, J. Economet. 210 (2019), no. 1, 58–74.
[6] Financial Stability Board, Financial Stability Implications from Fintech: Supervisory and Regulatory Issues that Merit Authorities’ Attention, Washington, DC: International Monetary Fund and World Bank, 2018.
[7] M.K. Brunnermeier and L.H. Pedersen, Market liquidity and funding liquidity, Rev. Financ. Stud. 22 (2009), no. 6, 2201–2238.
[8] O. De Bandt and P. Hartmann, Systemic risk: A survey, Available at SSRN 258430, (2000).
[9] M. Hatef Vahid and A. Saleh Ardestani, Systemic risk evaluation of banks and financial institutions applying Markov clustering method and centrality measures of risk, Islamic Economics & Banking, 9 (2020), no. 30, 115–140.
[10] X. Jin, How Much Does Book Value Data Tell us About Systemic Risk and its Interactions with the Macroeconomy? A Luxembourg Empirical Evaluation, Central Bank of Luxembourg, 2018.
[11] M.E. Kaukab, The urgency of foreign direct investment in micro, small, and medium enterprises financing framework: The case of Indonesia, Verslas: teorija ir praktika 24 (2023), no. 1, 47–57.
[12] E. Nier, J. Yang, T. Yorulmazer, and A. Alentorn, Network models and financial stability, J. Econ. Dyn. Control 31 (2007), no. 6, 2033–2060.
[13] X. Sun, X. Yao and J. Wang, Dynamic interaction between economic policy uncertainty and financial stress: A multi-scale correlation framework, Finance Res. Lett. 21 (2017), 214–221.
[14] N. Tarashev, Measuring portfolio credit risk correctly: Why parameter uncertainty matters, J. Bank. Finance 34 (2010), no. 9, 2065–2076.
[15] A.K. Tiwari, N. Trabelsi, F. Alqahtani, and S. Hammoudeh, Analysing systemic risk and time-frequency quantile dependence between crude oil prices and BRICS equity markets indices: A new look, Energy Econ. 83 (2019), 445–466.
[16] G.-J. Wang, Z.-Q. Jiang, M. Lin, C. Xie, and H.E. Stanley, Interconnectedness and systemic risk of China’s financial institutions, Emerg. Markets Rev. 35 (2018), 1–18.

Articles in Press, Corrected Proof
Available Online from 06 April 2024
  • Receive Date: 19 July 2022
  • Accept Date: 16 October 2022