In recent years, the context of the banking system, characterised by expansive monetary policies, has boosted the investments in leveraged loans. The COVID-19 pandemic brought the first real slowdown of the global economy since the financial crisis of 2007-08, and the growth of the leveraged loan market has been subject to significant attention from the competent authorities. Banks have remained solid despite the adverse outlook, however, the banking landscape continues to be impacted by the uncertainty relating to the evolution of the pandemic. The original sample for this paper, made up of leveraged loans, combines instrument-specific information with information on financial borrowing and the composition of the syndicate of banks/lenders. The aim of the paper is to identify a systemic risk indicator that takes into account the concentration of credit risk within each bank. For this purpose, using an M-quantile regression, it is possible to obtain an indicator (Mquantile coefficient) for each bank that varies between 0 and 1, where higher values indicate the greater presence of risky leveraged loans in that specific bank. Combined with an indicator of loan sharing between banks, this also allows a graphical representation of the network of banks in this specific market.
A systemic risk indicator for leveraged finance exposure in the banking system
Gennaro De Novellis
;Paola Musile Tanzi;
2022
Abstract
In recent years, the context of the banking system, characterised by expansive monetary policies, has boosted the investments in leveraged loans. The COVID-19 pandemic brought the first real slowdown of the global economy since the financial crisis of 2007-08, and the growth of the leveraged loan market has been subject to significant attention from the competent authorities. Banks have remained solid despite the adverse outlook, however, the banking landscape continues to be impacted by the uncertainty relating to the evolution of the pandemic. The original sample for this paper, made up of leveraged loans, combines instrument-specific information with information on financial borrowing and the composition of the syndicate of banks/lenders. The aim of the paper is to identify a systemic risk indicator that takes into account the concentration of credit risk within each bank. For this purpose, using an M-quantile regression, it is possible to obtain an indicator (Mquantile coefficient) for each bank that varies between 0 and 1, where higher values indicate the greater presence of risky leveraged loans in that specific bank. Combined with an indicator of loan sharing between banks, this also allows a graphical representation of the network of banks in this specific market.
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