Pietro Bogani, Matteo Fontana, Luca Neri, Simone Vantini
arXiv 1 Nov 2024 · Statistics — Methodology
arXiv:2411.00520 · PDF · DOI · OpenAlex · Extracted main text
Accurate computation of robust estimates for extremal quantiles of empirical distributions is an essential task for a wide range of applicative fields, including economic policymaking and the financial industry. Such estimates are particularly critical in calculating risk measures, such as Growth-at-Risk (GaR). % and Value-at-Risk (VaR). This work proposes a conformal framework to estimate calibrated quantiles, and presents an extensive simulation study and a real-world analysis of GaR to examine its benefits with respect to the state of the art. Our findings show that CP methods consistently improve the calibration and robustness of quantile estimates at all levels. The calibration gains are appreciated especially at extremal quantiles, which are critical for risk assessment and where traditional methods tend to fall short. In addition, we introduce a novel property that guarantees coverage under the exchangeability assumption, providing a valuable tool for managing risks by quantifying and controlling the likelihood of future extreme observations.
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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., and Giannone, D (2019) Vulnerable Growth | 0.874 | 19 | 2 | 100% |
| romano_conformalised_2019 | unmatched citation key romano_conformalised_2019 | 0.644 | 4 | 1 | 100% |
| 3 | Adrian, T., Grinberg, F., Liang, N., Malik, S., and Yu, J (2022) The Term Structure of Growth-at-Risk | 0.511 | 2 | 1 | 100% |
| 4 | Barber, R. F., Candès, E. J., Ramdas, A., and Tibshirani, R. J (2023) Conformal prediction beyond exchangeability | 0.405 | 1 | 1 | 100% |
| 5 | Brownlees, C. and Souza, A. B (2021) Backtesting global growth-at-risk | 0.405 | 1 | 1 | 100% |
| 6 | Goulet Coulombe, P., Leroux, M., Stevanovic, D., and Surprenant, S (2022) How is machine learning useful for macroeconomic forecasting? | 0.405 | 1 | 1 | 100% |
| 7 | Fontana, M., Zeni, G., and Vantini, S (2023) Conformal prediction: A Unified Review of Theory and New Challenges self | 0.405 | 1 | 1 | 100% |
| 8 | International Monetary Fund (2017) Global Financial Stability Report, October 2017: Is Growth at Risk? | 0.405 | 1 | 1 | 100% |
| 9 | Koenker, R. and Bassett, G (1978) Regression Quantiles | 0.405 | 1 | 1 | 100% |
| 10 | McNeil, A. J., Frey, R., Embrechts, P., and Frey, R. D (2010) Quantitative Risk Management: Concepts, Techniques, and Tools | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 14 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.