Kleinknecht, Manuel and Ng, Wing Lon (2014) Improving portfolio risk profile with threshold accepting. In: 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2014-03-27 - 2014-03-28, London.
Kleinknecht, Manuel and Ng, Wing Lon (2014) Improving portfolio risk profile with threshold accepting. In: 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2014-03-27 - 2014-03-28, London.
Kleinknecht, Manuel and Ng, Wing Lon (2014) Improving portfolio risk profile with threshold accepting. In: 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2014-03-27 - 2014-03-28, London.
Abstract
The application of the Threshold Accepting (TA) algorithm in portfolio optimisation can reduce portfolio risk compared with a Trust-Region local search algorithm. In a benchmark comparison of several different objective functions combined with different optimisation routines, we show that the TA search algorithm applied to a Conditional Value at Risk (CVaR) objective function yields the lowest Basel III market risk capital requirements. Not only does the TA algorithm outmatch the Trust-Region algorithm in all risk and performance measures, but when combined with a CVaR or 1% VaR objective function, it also achieves the best portfolio risk profile.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Science and Health > Computer Science and Electronic Engineering, School of > Centre for Computational Finance and Economic Agents |
| SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
| Depositing User: | Unnamed user with email elements@essex.ac.uk |
| Date Deposited: | 20 Jul 2015 08:25 |
| Last Modified: | 05 Dec 2024 12:10 |
| URI: | http://repository.essex.ac.uk/id/eprint/14336 |