Timo Dimitriadis, Yannick Hoga
arXiv 28 Jun 2022 · Econometrics · publishedJournal of Business and Economic Statistics (2025) · 1 citations (OpenAlex)
arXiv:2206.14275 · PDF · DOI · OpenAlex · Extracted main text
The popular systemic risk measure CoVaR (conditional Value-at-Risk) and its variants are widely used in economics and finance. In this article, we propose joint dynamic forecasting models for the Value-at-Risk (VaR) and CoVaR. The CoVaR version we consider is defined as a large quantile of one variable (e.g., losses in the financial system) conditional on some other variable (e.g., losses in a bank's shares) being in distress. We introduce a two-step M-estimator for the model parameters drawing on recently proposed bivariate scoring functions for the pair (VaR, CoVaR). We prove consistency and asymptotic normality of our parameter estimator and analyze its finite-sample properties in simulations. Finally, we apply a specific subclass of our dynamic forecasting models, which we call CoCAViaR models, to log-returns of large US banks. A formal forecast comparison shows that our CoCAViaR models generate CoVaR predictions which are superior to forecasts issued from current benchmark models.
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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 | Adrian, T. and Brunnermeier, M. K (2016) CoVaR | 1.000 | 9 | 3 | 100% |
| 2 | White, H., Kim, T.-H., and Manganelli, S (2015) VAR for VaR: Measuring tail dependence using multivariate regression quantiles | 0.928 | 5 | 4 | 80% |
| 3 | Catania, L. and Luati, A (2023) Semiparametric modeling of multiple quantiles | 0.928 | 5 | 3 | 80% |
| 4 | Fissler, T. and Hoga, Y (2024) Backtesting systemic risk forecasts using multi-objective elicitability self | 0.909 | 16 | 6 | 75% |
| 5 | Engle, R. F. and Manganelli, S (2004) CAViaR: Conditional autoregressive value at risk by regression quantiles | 0.867 | 23 | 10 | 65% |
| 6 | Patton, A. J., Ziegel, J. F., and Chen, R (2019) Dynamic semiparametric models for expected shortfall (and value-at-risk) | 0.750 | 19 | 9 | 42% |
| 7 | Engle, R. F (2002) Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models | 0.737 | 5 | 3 | 40% |
| 8 | Newey, W. K. and McFadden, D (1994) Large sample estimation and hypothesis testing | 0.737 | 4 | 3 | 50% |
| 9 | Hoga, Y. (2025+) (2025) The estimation risk in extreme systemic risk forecasts self | 0.737 | 3 | 3 | 67% |
| 10 | Diebold, F. X. and Mariano, R. S (1995) Comparing predictive accuracy | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 84 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Persistence-Robust Break Detection in Predictive CoVaR Regressions | 0.644 | 2 | 2 |
| 2 | Systemic Risk Surveillance | 0.405 | 1 | 1 |
| 3 | Statistical Inference for Score Decompositions | 0.000 | 1 | 1 |