In the post-pandemic era, the exposure to leveraged finance has emerged as a key factor of vulnerability for banks, coping with increasing inflation and interest rates. For this reason, the growth of the leveraged loans market is receiving significant attention from the Authorities (e.g. ECB, 2022). In this paper, we analyse an original sample of leveraged loans (1,699) that combines instrument-specific information and the composition of the syndicates, with a specific focus on the G-SIBs participation from 2014 to 2021. The aim is to identify risk indicators that take into account the G-SIBs exposure to risky leveraged loans, the potential impact of the banks’ size and their interconnectedness. For this purpose, using M-Quantile regression for binary data, it is possible to obtain a first indicator measuring heterogeneity among banks in terms of credit risk exposure, a second indicator that combines the previous one with the banks’ size, and a third indicator as a measure of interconnectedness between banks.

在后疫情时代，杠杆融资已成为银行应对通胀和利率上升的脆弱性的关键因素。因此，杠杆贷款市场的增长受到当局的高度关注（例如欧洲央行，2022）。在本文中，我们分析了杠杆贷款的原始样本 (1,699)，该样本结合了工具特定信息和银团的构成，特别关注 2014 年至 2021 年 G-SIB 的参与。目的是识别风险指标考虑到 G-SIB 的杠杆贷款风险敞口、银行规模及其相互关联性的潜在影响。为此，使用二元数据的 M-Quantile 回归，可以获得衡量银行间信用风险暴露异质性的第一个指标、将前一个指标与银行规模相结合的第二个指标以及第三个指标作为银行间互联程度的衡量标准。