A Theoretical and Empirical Comparison of Systemic Risk Measures
Abstract
We propose a theoretical and empirical comparison of the most popular systemic risk measures. To do so, we derive the systemic risk measures in a common framework and show that they can be expressed as linear transformations of firms' market risk (e.g., beta). We also derive conditions under which the different measures lead similar rankings of systemically important financial institutions (SIFIs). In an empirical analysis of US financial institutions, we show that (1) different systemic risk measures identify different SIFIs and that (2) firm rankings based on systemic risk estimates mirror rankings obtained by sorting firms on market risk or liabilities. One-factor linear models explain between 83% and 100% of the variability of the systemic risk estimates, which indicates that standard systemic risk measures fall short in capturing the multiple facets of systemic risk.
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