arXiv 8 Oct 2024 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2410.05861 · PDF · DOI · OpenAlex · Extracted main text
Forecasting risk (as measured by quantiles) and systemic risk (as measured by Adrian and Brunnermeiers's (2016) CoVaR) is important in economics and finance. However, past research has shown that predictive relationships may be unstable over time. Therefore, this paper develops structural break tests in predictive quantile and CoVaR regressions. These tests can detect changes in the forecasting power of covariates, and are based on the principle of self-normalization. We show that our tests are valid irrespective of whether the predictors are stationary or near-stationary, rendering the tests suitable for a range of practical applications. Simulations illustrate the good finite-sample properties of our tests. Two empirical applications concerning equity premium and systemic risk forecasting models show the usefulness of the tests.
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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 | 13 | 3 | 100% |
| 2 | Shao, X. and Zhang, X (2010) Testing for Change Points in Time Series | 0.941 | 6 | 3 | 83% |
| 3 | Zhang, T. and Lavitas, L (2018) Unsupervised Self-Normalized Change-Point Testing for Time Series | 0.928 | 5 | 3 | 80% |
| 4 | Lee, J. H (2016) Predictive Quantile Regression with Persistent Covariates: IVX-QR Approach | 0.894 | 7 | 4 | 71% |
| 5 | Han, H. and Linton, O. and Oka, T. and Whang Y. J (2016) The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability Between Time Series | 0.874 | 5 | 2 | 100% |
| 6 | Hoga, Y. and Schulz, C (2025) Self-Normalized Inference in (Quantile, Expected Shortfall) Regressions for Time Series self | 0.794 | 6 | 3 | 50% |
| 7 | Fan, R. and Lee, J. H (2019) Predictive Quantile Regressions under Persistence and Conditional Heteroskedasticity | 0.737 | 3 | 3 | 67% |
| 8 | Shao, X Self-Normalization for Time Series: A Review of Recent Developments | 0.737 | 3 | 3 | 67% |
| 9 | Magdalinos, T. and Phillips, P. C. B (2020) Econometric Inference in Matrix Vicinities of Unity and Stationarity | 0.727 | 26 | 5 | 38% |
| 10 | Brunnermeier, M. and Rother, S. and Schnabel, I (2020) Asset price bubbles and systemic risk | 0.693 | 5 | 1 | 100% |
Showing the top 10 of 74 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Quantile Granger Causality in the Presence of Instability | 0.405 | 1 | 1 |