Risk Measure Inference

Christophe Hurlin, Sébastien Laurent, Rogier Quaedvlieg and Stephan Smeekes

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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 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.

Keywords: Economie; quantitative (search for similar items in EconPapers)
Date: 2017-10
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Citations: View citations in EconPapers (7)

Published in Journal of Business and Economic Statistics, 2017, 35 (4), pp.499-512. ⟨10.1080/07350015.2015.1127815⟩

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Journal Article: Risk Measure Inference (2017)
Working Paper: Risk Measure Inference (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01457393

DOI: 10.1080/07350015.2015.1127815

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