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Risk Measure Inference

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 semi-parametric 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 form 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 ?CoVaR, 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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https://hal-amu.archives-ouvertes.fr/hal-01457393
Contributor : Patrice Cacciuttolo <>
Submitted on : Monday, February 6, 2017 - 2:18:24 PM
Last modification on : Wednesday, October 14, 2020 - 3:50:21 AM

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Christophe Hurlin, Sébastien Laurent, Rogier Quaedvlieg, Stephan Smeekes. Risk Measure Inference. Journal of Business and Economic Statistics, Taylor & Francis, 2017, 35 (4), pp.499-512. ⟨10.1080/07350015.2015.1127815⟩. ⟨hal-01457393⟩

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