Risk measure inference

C. Hurlin, S.F.J.A. Laurent, R. Quaedvlieg, S. 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 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.
Original languageEnglish
Pages (from-to)499-512
Number of pages14
JournalJournal of Business & Economic Statistics
Volume35
Issue number4
Early online date1 Jan 2016
DOIs
Publication statusPublished - 22 Sep 2017

Keywords

  • Bootstrap
  • Estimation risk
  • Grouped ranking
  • VALUE-AT-RISK
  • FALSE DISCOVERY RATE
  • BOOTSTRAP PREDICTION
  • VOLATILITY MODELS
  • GARCH MODELS
  • ARCH MODELS
  • SHORTFALL
  • INTERVALS
  • RANKING

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