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Robustness of banking networks to idiosyncratic and systemic shocks: a network-based approach

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Abstract

We propose a network-based structural model of credit risk to demonstrate how idiosyncratic and systemic shocks propagate across the banking system and evaluate the costs. The banking system is built as a network of heterogeneous banks which are connected with one another. In such a system, single credit events propagate through the interbank market from debtors to creditors and across the system. The shock is imposed as an unexpected event. We demonstrate that while idiosyncratic shocks cannot substantially disturb the banking system, a systemic shock of even a moderate magnitude can be highly detrimental. Such shock includes a huge contagious potential. We demonstrate that the costs of the shock are largely determined by the extent of contagion and range from negligible to catastrophic. The results imply that a severe crisis has to be initiated by a systemic shock of at least moderate magnitude. Capital ratio and the bank size are two additional factors of the banking system stability. Finally, credit risk analysis is sensitive to the network topology and exhibits a profound nonlinear characteristic.

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Notes

  1. In recent history, the default of Russian government debt in August 1998 sunk Long-Term Capital Management (LTCM), while the collapse of the subprime housing market in 2007 sunk Bear Stearns, Merrill Lynch and Lehman Brothers and many others, and continued into a sovereign debt crisis in Europe. Systemic events may take various forms, such as economic downturn, fall in the housing prices or in sovereign debt etc. Kindleberger and Aliber (2011) and Reinhart and Rogoff (2009) provide a thorough historical overview of financial crises.

  2. See Upper (2011) for the recent overview of the credit contagion literature.

  3. Tesfatsion and Judd (2006) and Steinbacher et al. (2014) provide thorough reviews of recent agent-based models in economics and finance.

  4. We use the banks’ Tier 1 capital levels instead of equity because the banks’ solvency is determined upon the former and not the latter, although the bank equity is among the core constituents of its Tier 1 capital. Tier 1 capital ratio is also the most frequently monitored and observed parameter in the banking industry. In the sequel, we interchangeable use the words of capital and Tier 1 capital to refer to Tier 1 capital.

  5. For a comprehensive overview of social networks, see Wasserman and Faust (1994) or Jackson (2010). Steinbacher et al. (2014) focus on the network-based models in economics and finance.

  6. No loops or multiple links are allowed.

  7. Eventual mutual exposures between the banks do not imply debt reconciliation.

  8. On the contrary, highly positive correlation between default rates and the GDP growth and the negative correlation between default rates and RR have been observed (Shleifer and Vishny 1992; Altman et al. 2005; Acharya et al. 2007). Shleifer and Vishny (2011) provide a discussion on asset fire sales in the perspective of the latest financial crisis.

  9. Iori et al. (2008), Schweitzer et al. (2009) and Allen and Babus (2009) argue that the network of major international financial institutions is strongly interdependent, exhibiting an increasing scale-free characteristic. Such networks are resilient to random failures, but susceptible to targeted attacks (Albert et al. 2000).

  10. There was no specific rule according to which the single banks were selected. However, to reflect the structure of the banking system, we wanted to have a few big banks, some medium-sized banks and a majority of smaller banks. Banks come from the US, UK, Switzerland, Germany, the Netherlands, Italy, Belgium, Japan, Austria and Slovenia.

  11. The real data on total assets and the Tier 1 capital is used to get a reflection of banks’ two the most important parameters. It should be emphasized that balance sheet structures from the paper do not reflect the actual balance sheets structures of corresponding banks, but are modified accordingly to meet the requirements of different network topologies and the banks’ initial data on total assets and the capital constraints. One can presume that different networks are associated with different sizes of banks’ interbank positions, which affects the size of banks’ non-trading assets.

  12. Note that \(RR<1\).

  13. These banks are the largest three and amount to $6,949.8 bln in banking assets and $854.4 bln in capital.

  14. Banks 2, 4, 20 sum up to $5,477.8 bln in banking assets and $633.8 bln in capital, which is significantly less than the combination 1, 2, 24.

  15. The notion of a risk-adjusted capital is in the spirit of such a safety shield rule.

  16. Similarly, Ladley (2013) finds that connectivity has stabilizing effects when the shock is small and works contagiously when it is large.

  17. A portfolio principle of Markowitz (1952) could well be applied here.

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Authors and Affiliations

  1. Kiel Institute for the World Economy, Kiellinie 66, 24105, Kiel, Germany

    Matjaž Steinbacher

  2. Faculty of Business Studies, Ciril-Metodov trg 9, 1000, Ljubljana, Slovenia

    Mitja Steinbacher

  3. University of Donja Gorica, Donja Gorica, 81000, Podgorica, Montenegro

    Matej Steinbacher

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  1. Matjaž Steinbacher
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Correspondence to Mitja Steinbacher.

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Steinbacher, M., Steinbacher, M. & Steinbacher, M. Robustness of banking networks to idiosyncratic and systemic shocks: a network-based approach. J Econ Interact Coord 11, 95–117 (2016). https://doi.org/10.1007/s11403-014-0143-3

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