Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability
Abstract
Systemic risk measures such as CoVaR, CoES and MES are widely-used in finance, macroeconomics and by regulatory bodies. Despite their importance, we show that they fail to be elicitable and identifiable. This renders forecast comparison and validation, commonly summarised as `backtesting', impossible. The novel notion of \emph{multi-objective elicitability} solves this problem. Specifically, we propose Diebold--Mariano type tests utilising two-dimensional scores equipped with the lexicographic order. We illustrate the test decisions by an easy-to-apply traffic-light approach. We apply our traffic-light approach to DAX~30 and S\&P~500 returns, and infer some recommendations for regulators.
Cite
@article{arxiv.2104.10673,
title = {Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability},
author = {Tobias Fissler and Yannick Hoga},
journal= {arXiv preprint arXiv:2104.10673},
year = {2023}
}
Comments
28 pages + 25 Appendix, 9 figures Structure improved; minor additions and corrections