Elicitability and identifiability of set-valued measures of systemic risk
研究了系统性风险集值度量的可识别性和可激励性，构建了定向选择性识别函数，并通过模拟验证了其在回测中评估风险度量校准和比较性能的适用性。
Abstract Identification and scoring functions are statistical tools to assess the calibration of risk measure estimates and to compare their performance with other estimates, e.g. in backtesting. A risk measure is called identifiable (elicitable) if it admits a strict identification function (strictly consistent scoring function). We consider measures of systemic risk introduced in Feinstein et al. (SIAM J. Financial Math. 8:672–708, 2017). Since these are set-valued, we work within the theoretical framework of Fissler et al. (preprint, available online at arXiv:1910.07912v2 , 2020) for forecast evaluation of set-valued functionals. We construct oriented selective identification functions, which induce a mixture representation of (strictly) consistent scoring functions. Their applicability is demonstrated with a comprehensive simulation study.