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In recent years, the leveraged loan market has experienced considerable growth, with the covenant-lite loan being the predominant agreement. The goal of this research is to assess whether the covenant-lite type reduces or increases the probability of default. Mediation analysis allows us to decompose the effect of balance sheet indicators on a default event into direct and indirect effects, the latter mediated by the covenant-lite. Results show that the covenant-lite is granted to borrowers with a greater profitability. In turn, all other conditions being equal, this agreement plays a role in making a default event less likely, giving rise to a significant indirect effect.

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 analyze an original sample of leveraged loans (1699) 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.

With reference to a stratified case–control (CC) procedure based on a binary variable of primary interest, we derive the expression of the distortion induced by the sampling design on the parameters of the logistic model of a secondary variable. This is particularly relevant when performing mediation analysis (possibly in a causal framework) with stratified case–control (SCC) data in settings where both the outcome and the mediator are binary. Despite being designed for parametric identification, our strategy is general and can be used also in a nonparametric context. With reference to parametric estimation, we derive the maximum likelihood (ML) estimator and the M-estimator of the joint outcome–mediator parameter vector. We then conduct a simulation study focusing on the main causal mediation quantities (i.e., natural effects) and comparing M- and ML estimation to existing methods, based on weighting. As an illustrative example, we reanalyze a German CC data set in order to investigate whether the effect of reduced immunocompetency on listeriosis onset is mediated by the intake of gastric acid suppressors.

In recent years, attention toward Environmental, Social and Governance (ESG) issues has become increasingly important in the investment decision-making process, prompting interest of investors, companies, regulators and researchers on the possible relationships between financial performances and sustainable variables. With the aim to increase our understanding of these relationships, we use a graphical modeling approach on the MSCI and Bloomberg sustainable dataset for years from 2017 to 2021. Our analysis shows that companies with a higher level of compliance with ESG standards have lower assets' volatility than others and are not penalized in terms of returns. Furthermore, the increasing level of mandatory disclosure within the European area, induced by the current regulation, has reduced the strength of the positive relationship between Disclosure Score and ESG Score. Moreover, the negative relationship between ESG Score and volatility remains consistent across temporal and geographic areas.