Does Tail Dependence Make A Difference In the ... - Boston College

that ∆CoV aR is more in line with the “too interconnected to fail” paradigm, and SRISK is more related to the
“too big to fail” paradigm. In contrast, MES offers a compromise between these two paradigms.
6 Concluding Remarks
The global financial crisis initiated in 2008 has altered the regulators’ awareness of fragility of financial system.
This paper develop a common framework based on copula model to estimate several widely used return-based
systemic risk measures: Delta Conditional Value at Risk (∆CoV aR) and its Modified (∆CoV aR ), Marginal
Expected Shortfall(MES) and its extension, systemic risk measure (SRISK). The nonlinear dependence of tail
risk is straightforward to be accommodated in the copula model. By eliminating the discrepancy of the marginal
distribution, the copula specification provide a flexibility to concentrate on the effect of the dependence structure
in the estimation of systemic risk. Our empirical studies shows that the nonlinear dependence of tail risk does
make a difference in the estimation of ∆CoV aR and MES. Simulation exercises reveal that ∆CoV aR originally
proposed by AB(2011) is in conflict with dependence measures. The modified version of ∆CoV aR and MES is
more consistent with dependence measures, which is line with economic intuition that stronger dependence strength
results in higher systemic risk measures. Furthermore, we found that the linear quantile regression estimation of
∆CoV aR proposed by Adrian and Brunnermeier (2011) is inadequate to completely capture the non-linear contagion
tail effect, which tends to underestimate systemic risk in the presence of lower tail dependence. We estimate
the systemic risk contributions of four financial industry sample consisting of a large number of institutions for the
sample period from January 2000 to December 2010, and we found that ∆CoV aR is observed to have a strong
correlation with tail dependence. In contrast, MES is found to have a strong empirical relationship with firms’
conditional CAPM β. SRISK, however, provides further connection with firms level characteristics by accounting
for information on market capitalization and liability. This stylized fact seems to imply that ∆CoV aR is more in
line with the “too interconnected to fail” paradigm, and SRISK is more related to the “too big to fail” paradigm.
In contrast, MES offers a compromise between these two paradigms.
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