We present a multilayer network model for credit risk assessment. Our model accounts for multiple connections between borrowers (such as their geographic location and their economic activity) and allows for explicitly modelling the interaction between connected borrowers. We develop a multilayer personalized PageRank algorithm that allows quantifying the strength of the default exposure of any borrower in the network. We test our methodology in an agricultural lending framework, where it has been suspected for a long time default correlates between borrowers when they are subject to the same structural risks. Our results show there are significant predictive gains just by including centrality multilayer network information in the model, and these gains are increased by more complex information such as the multilayer PageRank variables. The results suggest default risk is highest when an individual is connected to many defaulters, but this risk is mitigated by the size of the neighbourhood of the individual, showing both default risk and financial stability propagate throughout the network.
翻译:我们提出了一个多层次的信用风险评估网络模型。我们的模型记录了借款人之间的多重联系(如其地理位置和经济活动),并允许明确模拟关联借款人之间的互动。我们开发了一个多层次的个性化PageRank算法,允许量化网络中任何借款人违约风险的强度。我们在农业贷款框架中测试了我们的方法,长期怀疑存在同样的结构性风险的借款人违约关系。我们的结果显示,通过将核心多层网络信息纳入模型中,可以产生显著的预测收益,这些收益通过多层PageRank变量等更复杂的信息增加。结果显示,当个人与许多违约人连接时,默认风险最高,但这一风险因个人周边的规模而减轻,这表明默认风险和整个网络中金融稳定都在蔓延。