Konstantin Görgen, Abdolreza Nazemi, Melanie Schienle
arXiv 13 Jun 2022 · Econometrics
arXiv:2206.06026 · PDF · DOI · OpenAlex · Extracted main text
We address challenges in variable selection with highly correlated data that are frequently present in finance, economics, but also in complex natural systems as e.g. weather. We develop a robustified version of the knockoff framework, which addresses challenges with high dependence among possibly many influencing factors and strong time correlation. In particular, the repeated subsampling strategy tackles the variability of the knockoffs and the dependency of factors. Simultaneously, we also control the proportion of false discoveries over a grid of all possible values, which mitigates variability of selected factors from ad-hoc choices of a specific false discovery level. In the application for corporate bond recovery rates, we identify new important groups of relevant factors on top of the known standard drivers. But we also show that out-of-sample, the resulting sparse model has similar predictive power to state-of-the-art machine learning models that use the entire set of predictors.
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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 | Candès, E., Y. Fan, L. Janson, and J. Lv (2018) Panning for gold: ‘model-X' knockoffs for high dimensional controlled variable selection | 0.974 | 13 | 3 | 92% |
| 2 | Dai, R. and R. F. Barber (2016) The knockoff filter for FDR control in group-sparse and multitask regression | 0.928 | 5 | 3 | 80% |
| 3 | Hansen, P. R., A. Lunde, and J. M. Nason (2011) The model confidence set | 0.928 | 5 | 3 | 80% |
| 4 | Romano, Y., M. Sesia, and E. Candès (2020) Deep Knockoffs | 0.830 | 7 | 3 | 57% |
| 5 | Nazemi, A., F. Baumann, and F. J. Fabozzi (2022) Intertemporal defaulted bond recoveries prediction via machine learning self | 0.811 | 4 | 2 | 100% |
| 6 | Ren, Z., Y. Wei, and E. Candès (2021) Derandomizing Knockoffs | 0.811 | 4 | 2 | 100% |
| 7 | Nazemi, A., K. Heidenreich, and F. J. Fabozzi (2018) Improving corporate bond recovery rate prediction using multi-factor support vector regressions self | 0.737 | 3 | 2 | 100% |
| 8 | Barber, R. F. and E. J. Candès (2015) Controlling the false discovery rate via knockoffs | 0.644 | 3 | 2 | 67% |
| 9 | Kelly, B. T., S. Pruitt, and Y. Su (2019) Characteristics are covariances: A unified model of risk and return | 0.644 | 2 | 2 | 100% |
| 10 | Meinshausen, N. and P. Bühlmann (2010) Stability selection | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 33 scored citations.