arXiv 5 Nov 2020 · Econometrics · publishedQuantitative Finance (2023)
arXiv:2011.04278 · PDF · DOI · OpenAlex · Extracted main text
The existing approaches to sparse wealth allocations (1) are limited to low-dimensional setup when the number of assets is less than the sample size; (2) lack theoretical analysis of sparse wealth allocations and their impact on portfolio exposure; (3) are suboptimal due to the bias induced by an $\ell_1$-penalty. We address these shortcomings and develop an approach to construct sparse portfolios in high dimensions. Our contribution is twofold: from the theoretical perspective, we establish the oracle bounds of sparse weight estimators and provide guidance regarding their distribution. From the empirical perspective, we examine the merit of sparse portfolios during different market scenarios. We find that in contrast to non-sparse counterparts, our strategy is robust to recessions and can be used as a hedging vehicle during such times.
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| Reference | Intensity | Mentions | Sections | Main text | |
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| 1 | Ao, M., Yingying, L., and Zheng, X (2019) Approaching mean-variance efficiency for large portfolios | 1.000 | 8 | 4 | 100% |
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| 3 | van de Geer, S., Buhlmann, P., Ritov, Y., and Dezeure, R (2014) On asymptotically optimal confidence regions and tests for high-dimensional models | 0.928 | 15 | 5 | 80% |
| 4 | Callot, L., Caner, M., Önder, A. O., and Ulaşan, E (2019) A nodewise regression approach to estimating large portfolios | 0.916 | 13 | 5 | 77% |
| 5 | Meinshausen, N. and Bühlmann, P (2006) High-dimensional graphs and variable selection with the lasso | 0.874 | 6 | 2 | 100% |
| 6 | Li, J (2015) Sparse and stable portfolio selection with parameter uncertainty | 0.843 | 3 | 3 | 100% |
| 7 | Ledoit, O. and Wolf, M (2004) A well-conditioned estimator for large-dimensional covariance matrices | 0.737 | 3 | 2 | 100% |
| 8 | Cai, T., Liu, W., and Luo, X (2011) A constrained l1-minimization approach to sparse precision matrix estimation | 0.737 | 3 | 2 | 100% |
| 9 | Belloni, A. and Chernozhukov, V (2013) Least squares after model selection in high-dimensional sparse models | 0.693 | 5 | 1 | 100% |
| 10 | Belloni, A., Chernozhukov, V., and Kato, K (2015) Uniform post-selection inference for least absolute deviation regression and other z-estimation problems | 0.644 | 2 | 2 | 100% |
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| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Learning from Forecast Errors: A New Approach to Forecast Combinations | 0.644 | 2 | 2 |