Financial contagion among the GSIBs and regulatory interventions

Abstract

This paper compares three methods for assessing the contagion of risk among ten Globally Significant International Banks, known as GSIBs, listed on the New York Stock Exchange with daily and weekly data sets from 2007 to 2020, based on Machine Learning and Network Analysis. In particular we identify the banks which are the largest net sources or transmitters of risk, and net receptors of risk. We also examine the response of regulatory actions, in the form of fines and BIS Bin Classification for capital adequacy.

Under alternative risk measures, of Range Volatility (RV) of share prices, Credit Default Swap (CDS) premia, and Conditional Value at Risk ($\Delta$CoVar), there is a stronger and significant connection between Contagion and the BIS Bin classifications relative to the connections between Contagion and banking fines, either in the amount or frequency of the fines. These results show that BIS bin classifications respond positively to underlying signals of increased contagion in the form of Range Volatility (RV) and $\Delta$CoVar measures but not to CDS risk premia.