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 language | English |
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Pages (from-to) | 499-512 |
Number of pages | 14 |
Journal | Journal of Business & Economic Statistics |
Volume | 35 |
Issue number | 4 |
Early online date | 1 Jan 2016 |
DOIs | |
Publication status | Published - 22 Sept 2017 |
Keywords
- Bootstrap
- Estimation risk
- Grouped ranking
- VALUE-AT-RISK
- FALSE DISCOVERY RATE
- BOOTSTRAP PREDICTION
- VOLATILITY MODELS
- GARCH MODELS
- ARCH MODELS
- SHORTFALL
- INTERVALS
- RANKING