Systemic risk-efficient asset allocations: Minimization of systemic risk as a network optimization problem
Systemic risk is a multi-layer network phenomenon. Layers represent various types of direct financial exposure of various types, including interbank liabilities and derivative- or foreign exchange exposures. An important layer of systemic risk emerges through common asset holdings of financial institutions. Strongly overlapping portfolios lead to similar exposures that are caused by price movements of the underlying financial assets.
Based on the knowledge of individual portfolio holdings of financial agents, we quantify the systemic risk of overlapping portfolios. We then present an optimization procedure whereby we minimize the systemic risk in a given financial market by optimally rearranging overlapping portfolio networks. The optimization is performed under the constraints that the expected returns and risk of the individual portfolios are unchanged.
We explicitly demonstrate the power of the method on the overlapping portfolio network of sovereign exposure between major European banks, using data from the European Banking Authority stress test of 2016. Systemic risk can be reduced by more than a factor of two, without any detrimental effects for the individual banks. These results are confirmed by a simple simulation of fire sales in the government bond market. In particular, we show that the contagion probability is dramatically reduced in the optimized network. We comment on the efficiency of the network optimization approach in comparison to equity-injection-based ways to reduce systemic risk. To obtain the same risk levels that are obtained in the network optimization, it would be necessary to increase the actual available capital by two thirds. This shows the immense potential of network-based systemic risk management.
A. Pichler, S. Poledna, S. Thurner, Systemic risk-efficient asset allocations: Minimization of systemic risk as a network optimization problem, Journal of Financial Stability 52 (2021) 100890