An resilience optimal evaluation of financial portfolios implies having plausible hypotheses about the multiple interconnections between the macroeconomic variables and the risk parameters. In this article, we propose a graphical model for the reconstruction of the causal structure that links the multiple macroeconomic variables and the assessed risk parameters, it is this structure that we call stress testing network. In this model, the relationships between the macroeconomic variables and the risk parameter define a “relational graph” among their time‐series, where related time‐series are connected by an edge. Our proposal is based on the temporal causal models, but unlike, we incorporate specific conditions in the structure which correspond to intrinsic characteristics this type of networks. Using the proposed model and given the high‐dimensional nature of the problem, we used regularization methods to efficiently detect causality in the time‐series and reconstruct the underlying causal structure. In addition, we illustrate the use of model in credit risk data of a portfolio. Finally, we discuss its uses and practical benefits in stress testing.

中文翻译：

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通过图形因果模型重建压力测试网络

金融投资组合的弹性最佳评估意味着对宏观经济变量和风险参数之间的多重关联具有合理的假设。在本文中，我们提出了一种用于重构因果结构的图形模型，该模型将多个宏观经济变量与评估的风险参数联系起来，正是这种结构被我们称为压力测试网络。在该模型中，宏观经济变量和风险参数之间的关系在其时间序列之间定义了一个“关系图”，其中相关的时间序列由一条边连接。我们的建议基于时间因果模型，但与之不同的是，我们在结构中纳入了特定条件，这些条件对应于此类网络的固有特征。使用提出的模型并考虑到问题的高维性质，我们使用正则化方法来有效地检测时间序列中的因果关系并重建潜在的因果结构。另外，我们说明了模型在投资组合的信用风险数据中的使用。最后，我们讨论了其在压力测试中的用途和实际好处。