Abstract
We study a model for solvency contagion risk in financial networks, which allows the spread of contagion to occur before the point of default. This model can quantify systemic contagion loss through stress testing. In the usual case, only the total liabilities and total assets in such a network can be observed. To overcome this problem, we adopt a Gibbs sampling method to generate samples of the interbank liabilities matrix conditioning on the edges. This methodological approach is applied to a Chinese commercial bank network. Our results show that the systemic contagion losses of this network are highly dependent on the perceived exogenous recovery rate, especially when the external shock is strong. In the stress testing, we also analyze solvency contagion losses due to equity and exposure by decomposing the changes in contagion losses from 2008 to 2018 into several individual parts. We find that the contagion losses of the network exhibit a downward trend, indicating a more robust and stable network.
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