Risk Measure Inference

Christophe Hurlin, Sébastien Laurent, Rogier Quaedvlieg and Stephan Smeekes

Journal of Business & Economic Statistics, 2017, vol. 35, issue 4, 499-512

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.

Date: 2017
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Citations: View citations in EconPapers (9)

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Working Paper: Risk Measure Inference (2017)
Working Paper: Risk Measure Inference (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:35:y:2017:i:4:p:499-512

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DOI: 10.1080/07350015.2015.1127815

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