Papp, G;
Caccioli, F;
Kondor, I;
(2019)
Bias-variance trade-off in portfolio optimization under expected shortfall with l(2) regularization.
Journal of Statistical Mechanics: Theory and Experiment
, 2019
, Article 013402. 10.1088/1742-5468/aaf108.
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Abstract
The optimization of a large random portfolio under the expected shortfall risk measure with an regularizer is carried out by analytical calculation for the case of uncorrelated Gaussian returns. The regularizer reins in the large sample fluctuations and the concomitant divergent estimation error, and eliminates the phase transition where this error would otherwise blow up. In the data-dominated region, where the number of different assets in the portfolio is much less than the length of the available time series, the regularizer plays a negligible role even if its strength is large, while in the opposite limit, where the size of samples is comparable to, or even smaller than the number of assets, the optimum is almost entirely determined by the regularizer. We construct the contour map of estimation error on the versus plane and find that for a given value of the estimation error the gain in due to the regularizer can reach a factor of about four for a sufficiently strong regularizer.
| Type: | Article |
|---|---|
| Title: | Bias-variance trade-off in portfolio optimization under expected shortfall with l(2) regularization |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1742-5468/aaf108 |
| Publisher version: | https://doi.org/10.1088/1742-5468/aaf108 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 3.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10062662 |
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