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), 27-28 March 2014, London.

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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
Depositing User:	Jim Jamieson
Date Deposited:	20 Jul 2015 08:25
Last Modified:	20 Jul 2015 08:25
URI:	http://repository.essex.ac.uk/id/eprint/14336

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