Yannick Hoga, Christian Schulz
arXiv 14 Feb 2025 · Econometrics
arXiv:2502.10065 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes valid inference tools, based on self-normalization, in time series expected shortfall regressions and, as a corollary, also in quantile regressions. Extant methods for such time series regressions, based on a bootstrap or direct estimation of the long-run variance, are computationally more involved, require the choice of tuning parameters and have serious size distortions when the regression errors are strongly serially dependent. In contrast, our inference tools only require estimates of the (quantile, expected shortfall) regression parameters that are computed on an expanding window, and are correctly sized as we show in simulations. Two empirical applications to stock return predictability and to Growth-at-Risk demonstrate the practical usefulness of the developed inference tools.
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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 | Galvao, A. F. and Yoon, J (2024) HAC covariance matrix estimation in quantile regression | 1.000 | 17 | 5 | 100% |
| 2 | Gregory, K. B., Lahiri, S. N., and Nordman, D. J (2018) A smooth block bootstrap for quantile regression with time series | 1.000 | 15 | 5 | 100% |
| 3 | Adrian, T., Boyarchenko, N., and Giannone, D (2019) Vulnerable growth | 1.000 | 15 | 3 | 100% |
| 4 | Barendse, S (2023) Efficiently weighted estimation of tail and interquantile expectations | 1.000 | 13 | 4 | 100% |
| 5 | Goyal, A. and Welch, I (2008) A comprehensive look at the empirical performance of equity premium prediction | 1.000 | 6 | 3 | 100% |
| 6 | Shao, X (2010) A self-normalized approach to confidence interval construction in time series | 1.000 | 5 | 3 | 100% |
| 7 | Cenesizoglu, T. and Timmermann, A (2008) Is the distribution of stock returns predictable? | 0.928 | 4 | 3 | 100% |
| 8 | Patton, A. J., Ziegel, J. F., and Chen, R (2019) Dynamic semiparametric models for expected shortfall (and value-at-risk) | 0.874 | 7 | 2 | 100% |
| 9 | Dimitriadis, T. and Bayer, S (2019) A joint quantile and expected shortfall regression framework | 0.874 | 5 | 2 | 100% |
| 10 | Fitzenberger, B (1997) The moving blocks bootstrap and robust inference for linear least squares and quantile regressions | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 71 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 | 0.794 | 6 | 3 |