Risk Measure Inference Christophe Hurlin Sébastien Laurent Rogier Quaedvlieg Stephan Smeekes 10.6084/m9.figshare.4928837 https://tandf.figshare.com/articles/journal_contribution/Risk_Measure_Inference/4928837 <p>We propose a bootstrap-based test of the null hypothesis of equality of two firms’ conditional risk measures (RMs) at a single point in time. The test can be applied to a wide class of conditional risk measures issued from parametric or semiparametric models. Our iterative testing procedure produces a grouped ranking of the RMs, which has direct application for systemic risk analysis. Firms within a group are statistically indistinguishable from each other, but significantly more risky than the firms belonging to lower ranked groups. A Monte Carlo simulation demonstrates that our test has good size and power properties. We apply the procedure to a sample of 94 U.S. financial institutions using ΔCoVaR, MES, and %SRISK. We find that for some periods and RMs, we cannot statistically distinguish the 40 most risky firms due to estimation uncertainty.</p> 2017-04-27 21:27:48 Bootstrap Estimation risk Grouped ranking