Systemic risk, loosely defined, describes the risk that large parts of the financial system will collapse, leading to potentially far-reaching consequences both within and beyond the financial system. Such risks can materialize following shocks to relatively small parts of the financial system and then spread through various contagion channels. Assessing the systemic risk a bank poses to the system has thus become a central part of regulating its capital requirements.

As with conventional risk types, systemic risks need to be quantified. Currently global regulators propose a range of bank-specific indicators that measure size and interconnectedness to proxy systemic risk. Oxford Mathematician Christoph Siebenbrunner and colleagues tested to what extent such indicators are able to act as a proxy for different types of contagion effects in the financial system. They developed a model that allowed them to integrate domino-type network contagion effects with a market model for calculating the price impact of asset fire sales, and were able to demonstrate the existence of solutions to the resulting equations as well as providing algorithms to compute these solutions.

Testing the model empirically using real-world data, they compared the regulatory indicator set to the best-fitting alternative indicator set selected from a large universe of possible sets. The results showed that the regulatory indicator set represents a good selection of bank-specific indicators. However, bank-specific indicators alone are not able to capture the full extent of contagion effects, in particular for contagion channels that have far-reaching systemic impacts such as the effects of mark-to-market accounting in the presence of asset fire sales.


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