A network model for central counterparty liquidity risk stress testing under incomplete information

Most network models in the literature are academic in nature and assume perfect knowledge of nodes and linkages; the result is often simulations that do not reflect the limitations of real-world data. Such research can often make generic statements about the behavior of the entire system, but it is less useful in terms of risk management for a single institution that is part of the network. In practice, a central counterparty (CCP) ecosystem has a simpler network than the interbank network, because many bilateral linkages are replaced by direct many-to-one linkages to the CCP as the central clearer. In addition, the CCP is often the regulator in the ecosystem and thus has information visibility (of both exposures and financial resources) of all the participants. We put forth a realistic network model that maximizes the use of data available to a CCP in order to simulate credit default contagion. We study the resulting liquidity needs of the CCP required to avert the default of the CCP itself. This model combines default simulations and a bipartite network to give a loss distribution that is conditional on a stress state. This risk-based approach has many advantages over the Cover 2 standard approach of the CCP industry.