Mitic, Peter;
(2023)
Direct determination of Operational Value-at-Risk using Descriptive Statistics.
In:
IDEAL 2023: Intelligent Data Engineering and Automated Learning – IDEAL 2023.
(pp. pp. 120-129).
Springer: Cham, Switzerland.
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Abstract
Regression and machine learning methods are applied to the problem of Value-at-Risk determination in the context of financial Operational Risk, in order to determine an optimal technique that agrees sufficiently well with established Monte Carlo analyses. The annualised sum of operational losses is identified as the most significant statistical influence on Value-at-Risk, and a technique using it as a proxy for measured Value-at-Risk in a Test environment is formalised. The optimal stand-alone model is Generalized Additive, with approximately 61% success. The success rate can be enhanced to approximately 65% using a stacked model.
| Type: | Proceedings paper |
|---|---|
| Title: | Direct determination of Operational Value-at-Risk using Descriptive Statistics |
| Event: | 24th International Conference on Intelligent Data Engineering and Automated Learning |
| Location: | Evora Portugal |
| Dates: | 22 Nov 2023 - 24 Nov 2023 |
| ISBN-13: | 978-3-031-48231-1 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-48232-8_12 |
| Publisher version: | https://doi.org/10.1007/978-3-031-48232-8_12 |
| 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: | Operational Risk, Pickands-Balkema-deHaan, Descriptive Statistics, Value-at-Risk, Generalized Additive Model, Loss Distribution |
| 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/10179253 |
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