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Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability

Tobias Fissler, Yannick Hoga

arXiv 20 Apr 2021 · Finance — Risk Management · publishedJournal of Business and Economic Statistics (2023) · 26 citations (OpenAlex)

arXiv:2104.10673 · PDF · DOI · OpenAlex · Extracted main text

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 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.

Citation extraction

59
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142
in-text mentions
59
distinct cited
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17,240
main-text words

appendix boundary found by appendix_titled_section at “Supplement” · 52% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Bank for International Settlements (2019) Basel Framework0.92843100%
2Adrian T, Brunnermeier MK (2016) CoVaR0.87462100%
3Gneiting T (2011) Making and evaluating point forecasts0.8434375%
4Nolde N, Ziegel JF (2017) Elicitability and backtesting: Perspectives for banking regulation0.8307557%
5Fissler T, Ziegel JF (2016) Higher order elicitability and Osband's principle0.81711655%
6Acharya VV, Pedersen LH, Philippon T, Richardson M (2017) Measuring systemic risk0.81142100%
7Fissler T, Ziegel JF, Gneiting T (2016) Expected shortfall is jointly elicitable with value-at-risk: Implications for backtesting0.81142100%
8Banulescu-Radu D, Hurlin C, Leymarie J, Scaillet O (2021) Backtesting marginal expected shortfall and related systemic risk measures0.75414543%
9Creal D, Koopman SJ, Lucas A (2013) Generalized autoregressive score models with applications0.7374350%
10Giacomini R, White H (2006) Tests of conditional predictive ability0.7374350%

Showing the top 10 of 59 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1How to Compare Copula Forecasts?1.00073
2Systemic Risk Surveillance0.95075
3Dynamic CoVaR Modeling and Estimation0.909166
4Estimating Conditional Value-at-Risk with Nonstationary Quantile Predictive Regression Models0.69361
5Measurability of functionals and of ideal point forecasts0.64422
6Persistence-Robust Break Detection in Predictive CoVaR Regressions0.64422
7Self-Normalized Inference in (Quantile, Expected Shortfall) Regressions for Time Series0.51121
8Testing Forecast Rationality for Measures of Central Tendency0.40511
9Quantile Time Series Regression Models Revisited0.40511
10Statistical Inference for Score Decompositions0.40511