Aggregate and systemic risk in complex systems are emergent phenomena depending on two properties: the idiosyncratic risk of the elements and the topology of the network of interactions among them. While a significant attention has been given to aggregate risk assessment and risk propagation once the above two properties are given, less is known about how the risk is distributed in the network and its relations with its topology. We study this problem by investigating a large proprietary dataset of payments among 2.4M Italian firms, whose credit risk rating is known. We document significant correlations between local topological properties of a node (firm) and its risk. Moreover we show the existence of an homophily of risk, i.e. the tendency of firms with similar risk profile to be statistically more connected among themselves. This effect is observed when considering both pairs of firms and communities or hierarchies identified in the network. We leverage this knowledge to show the predictability of the missing rating of a firm using only the network properties of the associated node.

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企业支付网络和信用风险评级

复杂系统中的集合风险和系统风险是根据两个属性而出现的现象：元素的特质风险和元素之间相互作用的网络拓扑。虽然显著已经关注到总体风险评估和风险传播，一旦上述两个属性被赋予对于网络中的风险如何分布及其与拓扑的关系知之甚少。我们通过调查240万意大利信用风险评级已知的意大利公司中的大型专有付款数据集来研究此问题。我们记录了节点（公司）的局部拓扑特性与其风险之间的显着相关性。此外，我们显示了风险同质性的存在，即具有相似风险特征的公司在统计学上相互之间联系更紧密的趋势。当同时考虑成对的公司和社区或网络中确定的层次结构时，会观察到这种效果。我们仅利用关联节点的网络属性，利用这些知识来显示公司缺失评级的可预测性。