Mitic, Peter;
(2022)
Maximum Value-at-Risk.
In:
Intelligent Computing & Optimization: Proceedings of the 5th International Conference on Intelligent Computing and Optimization 2022 (ICO2022).
(pp. pp. 981-990).
Springer: Cham, Switzerland.
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Abstract
Some Value-at-risk estimates can be so large that their validity is questionable, and are subjectively rejected. An objective rejection criterion, based on a comparison of empirical data with a Generalised Pareto model of the data tail and applying the Pickands-Balkema-deHaan Theorem is presented. A consequent definition and measure of ’Maximum Value-at-risk’ is developed and validated.
| Type: | Proceedings paper |
|---|---|
| Title: | Maximum Value-at-Risk |
| Event: | International Conference on Intelligent Computing & Optimization 2022 |
| ISBN-13: | 978-3-031-19958-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-19958-5_92 |
| Publisher version: | https://doi.org/10.1007/978-3-031-19958-5_92 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Value-at-risk, Generalised pareto, Pickands-Balkema-deHaan Theorem, Single loss approximation, Order statistic |
| 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/10163230 |
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