Risk Measure Inference |
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| Authors: | Christophe Hurlin Sébastien Laurent Rogier Quaedvlieg Stephan Smeekes |
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| Institution: | 1. Laboratoire d’Economie d’Orléans (LEO), University of Orléans-CNRS, Orléans, France (christophe.hurlin@univ-orleans.fr);2. Aix-Marseille School of Economics, CNRS &3. EHESS, Aix-Marseille Graduate School of Management-IAE, Aix-Marseille University, France (sebastien.laurent@iae-aix.com);4. Department of Finance, Maastricht University, Maastricht, The Netherlands (r.quaedvlieg@maastrichtuniversity.nl);5. Department of Quantitative Economics, Maastricht University, Maastricht, The Netherlands (s.smeekes@maastrichtuniversity.nl) |
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| Abstract: | 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. |
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| Keywords: | Bootstrap Estimation risk Grouped ranking |
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