Timo Dimitriadis, Yannick Hoga
arXiv 22 Nov 2023 · Econometrics · publishedJournal of Business and Economic Statistics (2025) · 2 citations (OpenAlex)
arXiv:2311.13327 · PDF · DOI · OpenAlex · Extracted main text
We introduce a new regression method that relates the mean of an outcome variable to covariates, under the "adverse condition" that a distress variable falls in its tail. This allows to tailor classical mean regressions to adverse scenarios, which receive increasing interest in economics and finance, among many others. In the terminology of the systemic risk literature, our method can be interpreted as a regression for the Marginal Expected Shortfall. We propose a two-step procedure to estimate the new models, show consistency and asymptotic normality of the estimator, and propose feasible inference under weak conditions that allow for cross-sectional and time series applications. Simulations verify the accuracy of the asymptotic approximations of the two-step estimator. Two empirical applications show that our regressions under adverse conditions are a valuable tool in such diverse fields as the study of the relation between systemic risk and asset price bubbles, and dissecting macroeconomic growth vulnerabilities into individual components.
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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, Boyarchenko N, Giannone D (2019) Vulnerable growth | 1.000 | 12 | 3 | 100% |
| 2 | Brunnermeier M, Rother S, Schnabel I (2020) a | 0.985 | 22 | 7 | 95% |
| 3 | Brunnermeier MK, Dong GN, Palia D (2020) b | 0.969 | 11 | 5 | 91% |
| 4 | Fissler T, Hoga Y (2024) Backtesting systemic risk forecasts using multi-objective elicitability | 0.874 | 9 | 6 | 67% |
| 5 | Koenker R, Bassett G (1978) Regression quantiles | 0.843 | 4 | 3 | 75% |
| 6 | Adrian T, Brunnermeier MK (2016) CoVaR | 0.843 | 3 | 3 | 100% |
| 7 | Patton AJ, Ziegel JF, Chen R (2019) Dynamic semiparametric models for expected shortfall (and value-at-risk) | 0.830 | 7 | 4 | 57% |
| 8 | Acharya VV, Pedersen LH, Philippon T, Richardson M (2017) Measuring systemic risk | 0.811 | 4 | 2 | 100% |
| 9 | Newey WK, McFadden D (1994) Large sample estimation and hypothesis testing | 0.769 | 11 | 4 | 45% |
| 10 | Basel Committee on Banking Supervision (2019) Minimum capital requirements for market risk | 0.737 | 4 | 3 | 50% |
Showing the top 10 of 78 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Statistical Inference for Score Decompositions | 0.511 | 2 | 2 |
| 2 | Dynamic CoVaR Modeling and Estimation | 0.405 | 1 | 1 |