Financial Connectedness and Contagion Risk in a Dual Banking System
DOI :
https://doi.org/10.23882/emss25103Mots-clés :
Contagion risk, Financial connectedness, Complex networks, Dual banking systemRésumé
The 2008 subprime mortgage crisis laid bare the vulnerabilities inherent in financial interdependencies, where shocks originating from one sector and entity rapidly disseminated through complex networks, engendering a contagion effect that reverberated across the banking system. The present paper offers a comprehensive exploration of systemic risk, financial interconnectedness, and contagion within the context of a dual banking system. This appears to be a particular problem because of the heterogeneous market structure, which raises major questions about financial stability. Recognizing the profound implications of the interplay between Islamic and conventional banks, this study employs a comprehensive framework to dissect the intricate dynamics at play. The results reveal the existence of significant interconnectivity, which experiences a notable augmentation during periods of turmoil. Additionally, we provide an analysis of the topological structure of the interlinkage between Islamic and conventional banks, indicating substantial transmission of volatility, both unidirectionally and bidirectionally, across intersectoral and intrasectoral domains. However, our findings indicate that both incoming and outgoing connectivities are primarily influenced by the conventional banking sector.
Références
Abdul Manap, T. A. (2019). Measuring Systemic Risk in Dual Banking System: The Case of Malaysia. Islamic Finance, Risk-Sharing and Macroeconomic Stability. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-05225-6_8.
Abedifar, P., Giudici, P. & Hashem, S. Q. (2017). Heterogeneous Market Structure and Systemic Risk: Evidence from Dual Banking Systems. Journal of Financial Stability, 33: 6 — 119. https://doi.org/10.1016/j.jfs.2017.11.002.
Abuzayed, B. & Al-Fayoumi, N. (2021). Risk Spillover from Crude Oil Prices to GCC Stock Market Returns: New Evidence during the COVID-19 Outbreak. The North American Journal of Economics and Finance, 58. https://doi.org/10.1016/j.najef.2021.101476.
Acharya, V., Pedersen, L., Philippon, T. & Richardson, M. (2010). Measuring Systemic Risk. Technical Report, Department of Finance, New York University. http://dx.doi.org/10.2139/ssrn.1573171.
Acharya, V., Engle, R. & Richardson, M. (2012). Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks. American Economic Review, 102(3): 59 — 64. https://doi.org/10.1257/aer.102.3.59.
Addi, A. & Bouoiyour, J. (2023). Interconnectedness and Extreme Risk: Evidence from Dual Banking Systems. Economic Modelling, 120. https://doi.org/10.1016/j.econmod.2022.106150.
Adrian, T. & Brunnermeier, M. K. (2011). CoVaR. Federal Reserve Bank of New York. Staff Report No. 348. http://dx.doi.org/10.2139/ssrn.1269446.
Alqattan, A. A. & Alhayky, A. (2016). Impact of Oil Prices on Stock Markets: Evidence from Gulf Cooperation Council (GCC) Financial Markets. Amity Journal of Finance, 1(1): 1 — 8.
Antonakakis N., Chatziantoniou I., Gabauer D. (2020). Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13(4): 84. https://doi.org/10.3390/jrfm13040084.
Baruník, J. & Křehlík, T. (2018). Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk. Journal of Financial Econometrics, 16(2): 271 — 296. https://doi.org/10.1093/jjfinec/nby001.
Brownlees, C. & Engle, R. F. (2017). SRISK: A Conditional Capital Shortfall Measure of Systemic Risk. The Review of Financial Studies, 30(1): 48 — 79. https://doi.org/10.1093/rfs/hhw060.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Economics, 31(3): 307 — 327. https://doi.org/10.1016/0304-4076(86)90063-1.
Chatziantoniou, I. & Gabauer, D. (2021). EMU Risk-Synchronisation and Financial Fragility Through the Prism of Dynamic Connectedness. The Quarterly Review of Economics and Finance, 79, 1 — 14. https://doi.org/10.1016/j.qref.2020.12.003.
Caccioli, F., Farmer, J. D., Foti, N. & Rockmore, D. (2015). Overlapping Portfolios, Contagion, and Financial Stability. Journal of Economic Dynamics and Control, 51: 50 — 63. https://doi.org/10.1016/j.jedc.2014.09.041.
Demirer, M., Diebold, F. X., Liu, L. & Yilmaz, K. (2017). Estimating Global Bank Network Connectedness. Journal of Applied Econometrics, 33(1): 1 — 15. https://doi.org/10.1002/jae.2585.
Diebold, F. X. & Yilmaz, K. (2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting, 28(1): 57 –– 66. https://doi.org/10.1016/j.ijforecast.2011.02.006.
Diebold, F. X. & Yilmaz, K. (2014). On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms. Journal of Econometrics, 182(1): 1119 –– 134. https://doi.org/10.1016/j.jeconom.2014.04.012.
Engle, R. F. & Sheppard, K. (2001). Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH. National Bureau of Economic Research. Working Paper, 8554. https://doi.org/10.3386/w8554.
Engle, R. F. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business and Economic Statistics. https://doi.org/10.1198/073500102288618487.
Gabauer, D. (2020). Volatility Impulse Response Analysis for DCC-GARCH Models: The Role of Volatility Transmission Mechanisms. Journal of Forecasting, 39(5): 788 — 796. https://doi.org/10.1002/for.2648.
Härdle, W. K., Wang, W. & Yu, L. (2016). TENET: Tail-Event driven NETwork risk. Journal of Econometrics, 192(2): 499 — 513. https://doi.org/10.1016/j.jeconom.2016.02.013.
Koop, G., Pesaran, M. H. & Potter, S. M. (1996). Impulse Response Analysis in Nonlinear Multivariate Models. Journal of Econometrics, 74(1): 119 –– 147. https://doi.org/10.1016/0304-4076(95)01753-4.
Liu, X. (2016). Measuring Systemic Risk with Regime Switching in Tails. Economic Modelling, 67: 55 — 72. https://doi.org/10.1016/j.econmod.2016.09.015.
Maghyereh, A. I., Awartani, B. & Tziogkidis, P. (2017). Volatility Spillovers and Cross-Hedging between Gold, Oil and Equities: Evidence from the Gulf Cooperation Council Countries. Energy Economics, 68: 440 — 453. https://doi.org/10.1016/j.eneco.2017.10.025.
Pesaran, H. H. & Shin, Y. (1998). Generalized Impulse Response Analysis in Linear Multivariate Models. Economics Letters, 58(1): 17 –– 29. https://doi.org/10.1016/S0165-1765(97)00214-0.
White, H., Kim, T.-H. & Manganelli, S. (2015). VAR for VaR: Measuring Tail Dependence using Multivariate Regression Quantiles. Journal of Econometrics, 187(1): 169 — 188. https://doi.org/10.1016/S0165-1765(97)00214-0.
Yoon, S. M., Al Mamun, M., Salah Uddin, G. & Kang, S. H. (2019). Network Connectedness and Net Spillover between Financial and Commodity Markets. The North American Journal of Economics and Finance, 48: 801 — 818. https://doi.org/10.1016/j.jeconom.2015.02.004.
Zedda, S. & Spinace-Casale, A. (2021). Modeling and Simulating Cross Country Banking Contagion Risks. Journal of Risk and Financial Management, 14(8): 351. https://doi.org/10.3390/jrfm14080351.
Zhang, Z., Zhang, D., Wu, F. & Ji, Q. (2020). Systemic Risk in the Chinese Financial System: A Copula-based Network Approach. International Journal of Finance & Economics, 26(2): 2044 — 2063. https://doi.org/10.1002/ijfe.1892.
Zou, J., Fu, X., Yang, J. & Gong, C. (2022). Measuring Bank Systemic Risk in China: A Network Model Analysis. Systems, 10(1): 14. https://doi.org/10.3390/systems10010014.
Téléchargements
Publiée
Comment citer
Numéro
Rubrique
Licence
(c) Tous droits réservés Rachid Ouchchikh, Khalil Belghouat 2024
Ce travail est disponible sous licence Creative Commons Attribution - Pas d’Utilisation Commerciale 4.0 International.
