Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability

Tobias Fissler and Yannick Hoga

Papers from arXiv.org

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.

Date: 2021-04, Revised 2022-02
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Published in Journal of Business & Economic Statistics (2023)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2104.10673

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