Measuring systemic risk in the Moroccan banking system: A ∆CoVaR-based network approach
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Author(s)Ayoub KyoudLink to ORCID Index: https://orcid.org/0000-0001-8581-5716
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Mustapha BouchekourteLink to ORCID Index: https://orcid.org/0000-0003-1810-8805
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Cherif El MsiyahLink to ORCID Index: https://orcid.org/0000-0002-8028-5610
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Jaouad MadkourLink to ORCID Index: https://orcid.org/0000-0003-4109-0674
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DOIhttp://dx.doi.org/10.21511/bbs.21(2).2026.11
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Article InfoVolume 21 2026, Issue #2, pp. 150-162
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Creative Commons Attribution 4.0 International License
Type of the article: Research Article
Abstract
Systemic risk has emerged as a significant concern for financial stability, particularly in emerging markets that are susceptible to global financial disruptions. This paper examines the transmission channels of systemic risk within the Moroccan banking sector during significant crises, including the Subprime crisis, the European sovereign debt crisis, and the COVID-19 crisis. This study aims to characterize the Moroccan banking network, determine the key contributors to systemic risk, and analyze the mechanisms through which amplification loops exacerbate systemic risk under stressed market conditions. The complex dynamics of systemic risk transmission are captured by the ∆Conditional Value at Risk approach, which is represented as a directed weighted network, with topology indicators capturing the network position of financial institutions. The results indicate a pronounced core–periphery network, in which Attijariwafa Bank (AWB), Bank of Africa (BOA), and Banque Centrale Populaire (BCP) consistently form significant triangular feedback loops that amplify systemic risk across all examined periods. In-strength and out-strength centrality measures confirm their dominant positions as primary transmitters and receivers of systemic risk. In contrast, peripheral institutions play a comparatively less pronounced role within the network. Overall, the results point to a marked structural concentration of systemic risk within Morocco’s banking network and provide important implications for regulators and policymakers aiming to strengthen macroprudential oversight and safeguard financial stability.
- Keywords
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JEL Classification (Paper profile tab)G01, C32, C31, G21
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References33
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Tables6
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Figures3
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- Figure 1. ΔCoVaR-based interbank network for the Subprime crisis period
- Figure 2. ΔCoVaR-based interbank network for the European debt crisis period
- Figure 3. ΔCoVaR-based interbank network for the COVID-19 crisis period
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- Table 1. Kupiec test results
- Table 2. Diebold-Mariano test results
- Table 3. Distribution of network metrics of the Moroccan banking network (Subprime crisis)
- Table 4. Node-level centrality ranking of Moroccan banks across different crisis periods
- Table 5. Distribution of network metrics of the Moroccan banking network (European Debt Crisis)
- Table 6. Distribution of network metrics of the Moroccan banking network (COVID-19 Crisis)
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- Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564-608.
- Acharya, V. V., Cooley, T., Richardson, M., & Walter, I. (2010). Manufacturing tail risk: A perspective on the financial crisis of 2007–2009. Foundations and Trends in Finance, 4(4), 247-325.
- Adrian, T., & Brunnermeier, M. K. (2011). CoVaR (Working Paper No. 17454). National Bureau of Economic Research.
- Adrian, T., & Brunnermeier, M. K. (2016). CoVaR. The American Economic Review, 106(7), 1705-1741.
- Aldasoro, I., & Alves, I. (2018). Multiplex interbank networks and systemic importance: An application to European data. Journal of Financial Stability, 35, 17-37.
- Allen, F., & Gale, D. (2000). Financial contagion. Journal of Political Economy, 108(1), 1-33.
- Battiston, S., Puliga, M., Kaushik, R., Tasca, P., & Caldarelli, G. (2012). Debtrank: Too central to fail? Financial networks, the FED and systemic risk. Scientific Reports, 2, 541.
- Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104(3), 535-559.
- Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
- Brownlees, C., & Engle, R. F. (2017). SRISK: A conditional capital shortfall measure of systemic risk. The Review of Financial Studies, 30(1), 48-79.
- Cannon, A. J. (2011). Quantile regression neural networks: Implementation in R and application to precipitation downscaling. Computers & Geosciences, 37(9), 1277-1284.
- Cetorelli, N., & Goldberg, L. S. (2012). Banking globalization and monetary transmission. The Journal of Finance, 67(5), 1811-1843.
- Chao, S. K., Härdle, W. K., & Wang, W. (2015). Quantile regression in risk calibration. In C. F. Lee & J. Lee (Eds.), Handbook of financial econometrics and statistics (pp. 1467-1489). New York, NY: Springer.
- Claessens, S., & Van Horen, N. (2015). The impact of the global financial crisis on banking globalization. IMF Economic Review, 63, 868-918.
- Diebold, F. X., & Mariano, R. S. (2002). Comparing predictive accuracy. Journal of Business & Economic Statistics, 20(1), 134-144.
- Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119-134.
- Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
- Engle, R. F., & Manganelli, S. (2004). CAViaR: Conditional autoregressive value at risk by regression quantiles. Journal of Business & Economic Statistics, 22(4), 367-381.
- Fernandez, V. (2003). Extreme value theory and value at risk. Revista de Análisis Económico – Economic Analysis Review, 18(1), 57-85.
- Freixas, X., Parigi, B. M., & Rochet, J. C. (2000). Systemic risk, interbank relations, and liquidity provision by the central bank. Journal of Money, Credit and Banking, 32(3), 611-638.
- Haldane, A. G., & May, R. M. (2011). Systemic risk in banking ecosystems. Nature, 469, 351-355.
- Härdle, W. K., Wang, W., & Yu, L. (2016). Tenet: Tail-event driven network risk. Journal of Econometrics, 192(2), 499-513.
- Huang, W. Q., & Wang, D. (2018). A return spillover network perspective analysis of Chinese financial institutions’ systemic importance. Physica A: Statistical Mechanics and its Applications, 509, 405-421.
- Investing.com. (n.d.). Stock Quotes.
- Keilbar, G., & Wang, W. (2022). Modelling systemic risk using neural network quantile regression. Empirical Economics, 62, 93-118.
- Koenker, R. (2005). Quantile regression. Cambridge University Press.
- Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models. The Journal of Derivatives, 3(2), 73-84.
- McNeil, A. J., & Frey, R. (2000). Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Journal of Empirical Finance, 7(3-4), 271-300.
- Moratis, G., & Sakellaris, P. (2021). Measuring the systemic importance of banks. Journal of Financial Stability, 54, 100878.
- Nechba, Z. B. (2021). Moroccan conventional banks’ contribution to systemic risk. IJBTSR International Journal of Business and Technology Studies and Research, 3(3), 1-10.
- Poledna, S., Molina-Borboa, J. L., Martínez-Jaramillo, S., Van Der Leij, M., & Thurner, S. (2015). The multi-layer network nature of systemic risk and its implications for the costs of financial crises. Journal of Financial Stability, 20, 70-81.
- Said, K., EL Qalli, Y., & Fadlallah, A. (2023). Quantifying systemic risk in Morocco’s banking system using Euler indicators and extreme dependence. Cogent Business & Management, 10(3), 2278256.
- Zakaria, F. (2015). Systemic risk and financial contagion in morocco: New approaches of quantification. In J. W. Kensinger (Ed.), Overlaps of Private Sector with Public Sector around the Globe Emerald Group. Publishing Limited.
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The role of financial inclusion in financial stability: lesson from Jordan
Mohammad O. Al-Smadi
doi: http://dx.doi.org/10.21511/bbs.13(4).2018.03
Banks and Bank Systems Volume 13, 2018 Issue #4 pp. 31-39 Views: 3323 Downloads: 761 TO CITE АНОТАЦІЯThis study aims to examine the relationship between financial inclusion and financial stability in Jordan by using Fully Modified Least Squares (FMOLS) technique. The analysis is based on time series from 2006 to 2017. Jordanian financial inclusion index is developed to assess the level of financial inclusion, whereas financial stability was measured by Jordanian financial stability index proposed by Central Bank of Jordan. The results show a weak significant and positive impact of financial inclusion on the financial stability in Jordan. Additionally, five control variables are used in the study. The results show a negative impact of domestic credit to private sector, income inequality, financial integration, and global financial crisis on financial stability. In contrast, real GDP per capita has a significant and positive impact. It is expected that the findings of the study can be used by policy makers and supervising authorities to realize the objectives of the national strategy of financial inclusion in Jordan.
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The role of Islamic banks in promoting economic growth and financial stability: Evidence from Saudi Arabia
Faycal Chiad ,
Abdelhalim Gherbi
doi: http://dx.doi.org/10.21511/imfi.21(3).2024.29
Investment Management and Financial Innovations Volume 21, 2024 Issue #3 pp. 357-369 Views: 3225 Downloads: 718 TO CITE АНОТАЦІЯThe aim of this study is to provide a suitable empirical framework for the interaction between Islamic finance, financial stability, and economic development. Additionally, it is an attempt to empirically evaluate how the levels of financial system stability and economic growth in an oil-rich nation are affected by the financing provided by Islamic banks. The study employs the fully modified ordinary least squares (FMOLS) and quantile regression (QR) based on quarterly data from 2013 to 2022. The findings indicate strong evidence that Islamic banking finance supports economic growth and improves financial system stability. Moreover, the study highlights that this positive relationship is negatively affected by inflation rates and levels of economic policy uncertainty. Financial inclusion has an important positive impact on both dependent variables, reinforcing this link. Furthermore, oil rents in Saudi Arabia (KSA) have contributed to improving economic development and supporting the financial sector’s development to achieve economic diversification as outlined in the Saudi Vision 2030. These findings confirm the necessity of paying attention to developing Islamic banking and increasing its market share by creating products and services that achieve economic efficiency in accordance with suitable policies for making the financial sector a strategic sector that supports economic development in KSA.
Acknowledgment
This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-RPP2023024). -
Impact of determinants of the financial distress on financial sustainability of Ethiopian commercial banks
Kishor Meher 
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Henok Getaneh
doi: http://dx.doi.org/10.21511/bbs.14(3).2019.16
Banks and Bank Systems Volume 14, 2019 Issue #3 pp. 187-201 Views: 2789 Downloads: 756 TO CITE АНОТАЦІЯThe study aims to investigate the impact of determinants of financial distress on financial sustainability of Ethiopian commercial banks. The balanced panel data of 12 commercial banks of Ethiopia have been taken for the study from 2011 to 2017. The research deploys Ordinary Least Square (OLS) Regression Model. The indicators of financial distress are bank’s specific internals and macro-economic factors. The proxies of financial sustainability are Return on Assets, Return on Equity, Financial Stability Index and Bank Soundness. The findings reveal that the Absolute Liquidity Risk and Net Income Growth are found to be positive and significant and Solvency Risk negative and significant in relation to Return on Assets. Asset Quality is found to be positive and significant and Solvency Risk negative and significant with respect to Return on Equity. The Asset Quality and Net Income Risk are positive and significant and Solvency Risk is negative and significant with relation to the Financial Stability Index. Absolute Liquidity Risk and Liquidity Risk are positive and significant and Credit Risk negative and significant with Bank Soundness. Free Cash Flow and Net Income Growth are essential for enhancing Return on Assets and Bank Soundness, and managing equity within the prudential norms could bring forth short-term financial sustainability of commercial banks. By lowering provisioning of loan loss, Growth in Net Interest Income and managing Solvency Risk could ensure financial stability to the banks, which in turn leads to financial sustainability. The study reveals that financial sustainability of banks is insulated from the exposures of systematic risks originating from macroeconomic factors.
