Management of systemic risk in financial markets is traditionally associated with setting (higher) capital requirements for market participants. There are indications that while equity ratios have been increased massively since the financial crisis, systemic risk levels might not have lowered, but even increased (see ECB data1; SRISK time series2). It has been shown that systemic risk is to a large extent related to the underlying network topology of financial exposures. A natural question arising is how much systemic risk can be eliminated by optimally rearranging these networks and without increasing capital requirements. Overlapping portfolios with minimized systemic risk which provide the same market functionality as empir- ical ones have been studied by Pichler et al. (2018). Here we propose a similar method for direct exposure networks, and apply it to cross-sectional interbank loan networks, consisting of 10 quarterly observations of the Austrian interbank market. We show that the suggested framework rearranges the network topology, such that systemic risk is reduced by a factor of approximately 3.5, and leaves the relevant economic features of the optimized network and its agents unchanged. The presented optimization procedure is not intended to actually re-configure interbank markets, but to demonstrate the huge potential for systemic risk manage- ment through rearranging exposure networks, in contrast to increasing capital requirements that were shown to have only marginal e ects on systemic risk (Poledna et al., 2017). Ways to actually incentivize a self- organized formation toward optimal network configurations were introduced in Thurner and Poledna (2013) and Poledna and Thurner (2016) For regulatory policies concerning financial market stability the knowledge of minimal systemic risk for a given economic environment can serve as a benchmark for monitoring actual systemic risk in markets.
Diem, C., Pichler, A. & Thurner, S. (2019). 'What is the Minimal Systemic Risk in Financial Exposure Networks?'. INET Oxford Working Paper No. 2019-03.