High systemic risk in financial systems? Re-arrange their networks!
After major banking crises, like the big crash in 2008, the world is always calling for better regulations. Traditionally, the focus was on increasing the equity capital of banks. Researchers at the Complexity Science Hub Vienna (CSH) question this stand-alone approach as it neglects the networks structure of the system. In a paper, recently published in the Journal of Economic Dynamics and Control, they investigated how much the damage of a systemic crash can be reduced solely by re-arranging the mutual connections between institutions. Their method shows “a surprisingly high optimization potential of up to 70 percent,” says PhD candidate Christian Diem, the first author of the study.
Re-arranging bank connections
“Banks don’t stand alone. They are linked to, and strongly dependent on, each other by financial contracts, for instance by credits,” Christian explains. Links create networks—in this case the interbanking network. Complexity science has proven that the systemic risk of any single bank as well as of the entire financial network depends on the distribution of links.
“We developed optimization algorithms to reallocate banking loans,” co-author Anton Pichler, one of our Junior Fellows, points out. “To avoid possible negative effects caused by this process, we added special conditions to the algorithms,” he says. On the one hand the scientists wanted to assure that the individual risk of a bank does not increase, on the other hand, that the total amount of loans in the network does not decline.
To test their approach, Christian, Anton, and co-author Stefan Thurner run their algorithms on an anonymized data set of the 70 largest banks in Austria. Together, these banks covered 71 percent of the country’s interbank market volume in the years 2008 to 2010.
They found that the systemic risk was especially high when large banks were closely linked. Yet, by re-allocating the lending partners, an entirely new network emerges: with a far lower risk for systemic failure, while the amount of money lent and borrowed in the network remains the same.
Systemic risk reduction
The term systemic risk describes the hypothetical effect of individual bank failures on the entire banking system. An example is the bankruptcy of Lehman Brothers in 2008. “Links allow a domino effect: The collapsing institution affects the other banks it is connected to, these banks, again, affect their neighbouring banks, and so on,” Anton explains. In a network with high systemic risk, the failure of a single knot has the potential to crush the whole system. This can be avoided by the redistribution of links. “High equity ratios in banks may make the falling of a single domino piece less likely. But if a bank collapses nonetheless, our method helps the other pieces not to fall as well,” adds Christian.
New ways of regulations
The findings have far-reaching implications for regulators and supervisory bodies such as the European Banking Authority EBA or the European Central Bank ECB.
“To reduce collapse risk, regulators must start with the networks,” emphasizes Stefan. “To achieve a comparable risk reduction of 70 percent by the increase of bank equity, you would have to raise equity levels by another 230 percent on average. This would be a huge burden on banks.”
Instead, the new approach could be of use for monitoring existing regulations: Do they actually reduce the systemic risk and make the network safer? “Regulators could even create a new kind of incentive to influence decision-making processes in the banks. We could design these incentives in a way that automatically leads to safer networks,” concludes the Hub president. An example for such a regulation is the Systemic Risk Tax (SRT) Stefan has proposed together with a colleague in 2016. The Systemic Risk Tax has shown to reduce the risk for a systemic collapse by up to 50 percent.
References:
C. Diem, A. Pichler, S. Thurner, What is the minimal systemic risk in financial exposure networks?, Journal of Economic Dynamics and Control Vol. 116 July (2020) 103900
S. Poledna, S. Thurner, Elimination of systemic risk in financial networks by means of a systemic risk transaction tax, Quantitative Finance Volume 16, Issue 10 (2016) 1599–1613