Timo Dimitriadis, Yannick Hoga
arXiv 13 Jan 2026 · Econometrics
arXiv:2601.08598 · PDF · DOI · OpenAlex · Extracted main text
Following several episodes of financial market turmoil in recent decades, changes in systemic risk have drawn growing attention. Therefore, we propose surveillance schemes for systemic risk, which allow to detect misspecified systemic risk forecasts in an "online" fashion. This enables daily monitoring of the forecasts while controlling for the accumulation of false test rejections. Such online schemes are vital in taking timely countermeasures to avoid financial distress. Our monitoring procedures allow multiple series at once to be monitored, thus increasing the likelihood and the speed at which early signs of trouble may be picked up. The tests hold size by construction, such that the null of correct systemic risk assessments is only rejected during the monitoring period with (at most) a pre-specified probability. Monte Carlo simulations illustrate the good finite-sample properties of our procedures. An empirical application to US banks during multiple crises demonstrates the usefulness of our surveillance schemes for both regulators and financial institutions.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Hoga, Yannick and Demetrescu, Matei (2023) Monitoring value-at-risk and expected shortfall forecasts self | 1.000 | 6 | 3 | 100% |
| 2 | Fissler, Tobias and Hoga, Yannick (2024) Backtesting systemic risk forecasts using multi-objective elicitability self | 0.950 | 7 | 5 | 86% |
| 3 | Banulescu-Radu, Denisa and Hurlin, Christophe and Leymarie, Jeremy a… (2021) Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures | 0.928 | 5 | 5 | 80% |
| 4 | Engle, R. F (2002) Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models | 0.928 | 4 | 3 | 100% |
| 5 | Wang, Qiuqi and Wang, Ruodu and Ziegel, Johanna (2025) E-backtesting | 0.843 | 3 | 3 | 100% |
| 6 | Adrian, T. and Brunnermeier, M. K (2016) CoVaR | 0.811 | 4 | 2 | 100% |
| 7 | Hoga, Y (2019) Extending the Limits of Backtesting via the `Vanishing $p$'-Approach self | 0.644 | 2 | 2 | 100% |
| 8 | Krämer, W. and Wied, D (2015) A Simple and Focused Backtest of Value at Risk | 0.585 | 3 | 1 | 100% |
| 9 | Laurent, S. and Rombouts, J. V. K. and Violante, F (2012) On the forecasting accuracy of multivariate GARCH models | 0.585 | 3 | 1 | 100% |
| 10 | Laurent, S. and Rombouts, J. V. K. and Violante, F (2013) On loss functions and ranking forecasting performances of multivariate volatility models | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 46 scored citations.