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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 (ΔCoVar), 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 ΔCoVar measures but not to CDS risk premia.

This paper develops and estimates a dynamic stochastic general equilibrium (DSGE) model representing several key characteristics of Japan, namely, a large open economy, with large fiscal deficits and increasing amounts of debt held by domestic residents, through recurring sets of adverse shocks to productivity and positive shocks to government spending. We compare optimal simple rules for consumption tax rates, a Taylor rule with negative interest rates, and a quantitative easing rule, for reducing government debt held by the banking system, as well as optimizing welfare. In times of crisis, we show that the QE policy rule outperforms optimally-derived simple tax-rate rules or Taylor rules with negative interest rates for mitigating the costs of post-crisis adjustment and debt overhang.