Elicitability and Identifiability of Systemic Risk Measures

Abstract

Identification and scoring functions are statistical tools to assess the calibration and the relative performance of risk measure estimates, e.g., in backtesting. A risk measures is called identifiable (elicitable) it it admits a strict identification function (strictly consistent scoring function). We consider measures of systemic risk introduced in Feinstein, Rudloff and Weber (2017). Since these are set-valued, we work within the theoretical framework of Fissler, Hlavinová and Rudloff (2019) 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.

42 pages, 3 figures + supplementary material (6 pages, 2 figures)

References in corpus (4)

• The role of the information set for forecasting - with applications to risk management
• Evaluating Range Value at Risk Forecasts
• Supplement to "Erratum: Higher Order Elicitability and Osband's Principle"
• Optimal solutions to the isotonic regression problem

Cited by in corpus (1)

• Forecast Evaluation of Quantiles, Prediction Intervals, and other Set-Valued Functionals