发表机构
SKEMA Business School; Université Côte d’Azur; University of Pennsylvania; Koç University（SKEMA商学院; 蔚蓝海岸大学; 宾夕法尼亚大学; 科奇大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究扩展聚类连通性框架，引入控制变量聚类并开发排序敏感性诊断，应用于71家全球银行，显著提升稳健性与可解释性。
AI 中文摘要
我们将Buchwalter、Diebold和Yilmaz（2026）的聚类连通性框架向两个互补方向扩展，以提高跨聚类连通性的稳健性和可解释性。首先，我们通过刻画所有可容许识别排序下聚类层面净连通性的分布，开发了一种残差排序敏感性诊断方法，特别是通过配对首位和末位排序，同时保持所有其他聚类的相对顺序不变。其次，我们引入一个专门的控制变量聚类，以吸收与观测到的共同宏观金融因素相关的变异，同时保持聚类框架的计算可扩展性。控制聚类被固定在最前面，银行创新相对于其进行残差化处理，然后剩余的银行聚类照常进行排列和正交化，从而不改变可容许的银行聚类识别策略数量。在控制聚类创新对银行聚类创新具有同期外生性的递归假设下，银行聚类之间剩余的跨聚类连通性可以被解释为扣除那些观测到的共同因素冲击后的银行间传导。我们将该方法应用于2003年至2024年间分组为七个区域聚类的七十一家全球银行。共同宏观金融因素的处理显著影响了系统范围内的跨组连通性和聚类层面的净头寸。将控制变量置于专门的第一个聚类中，也大幅降低了所有七个银行聚类的首位与末位配对排序敏感性，其中美国和欧洲聚类的降幅尤为显著。
英文摘要
We extend the clustered connectedness framework of Buchwalter, Diebold and Yilmaz (2026) in two complementary directions that improve the robustness and interpretability of cross-cluster connectedness. First, we develop a diagnostic for residual ordering sensitivity by characterizing the distribution of cluster-level net connectedness across all admissible identification orderings and, in particular, by pairing first- and last-position orderings while holding fixed the relative ordering of all other clusters. Second, we introduce a dedicated cluster of control variables to absorb variation associated with observed common macro-financial factors while preserving the computational scalability of the clustered framework. The control cluster is fixed first, and bank innovations are residualized with respect to it before the remaining bank clusters are permuted and orthogonalized as usual, leaving the number of admissible bank-cluster identification strategies unchanged. Under the maintained recursive assumption that control-cluster innovations are contemporaneously exogenous to bank-cluster innovations, the remaining cross-cluster connectedness among the bank clusters can be interpreted as bank-to-bank transmission net of those observed common-factor shocks. We apply the methodology to seventy-one global banks grouped into seven regional clusters over 2003--2024. The treatment of common macro-financial factors materially affects both system-wide cross-group connectedness and cluster-level net positions. Placing the controls in a dedicated first cluster also substantially reduces paired first-versus-last ordering sensitivity across all seven bank clusters, with especially large reductions for the United States and the European clusters.
