DocumentCode :
120796
Title :
Improving portfolio risk profile with threshold accepting
Author :
Kleinknecht, Manuel ; Wing Lon Ng
Author_Institution :
Centre for Comput. Finance & Econ. Agents, Univ. of Essex, Colchester, UK
fYear :
2014
fDate :
27-28 March 2014
Firstpage :
92
Lastpage :
99
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.
Keywords :
investment; optimisation; risk management; search problems; stock markets; Basel III market risk capital requirements; CVaR objective function; TA search algorithm; conditional value at risk objective function; portfolio optimisation; portfolio risk profile improvement; threshold accepting algorithm; trust-region local search algorithm; Heuristic algorithms; Investment; Linear programming; Optimization; Portfolios; Reactive power; Standards;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Financial Engineering & Economics (CIFEr), 2104 IEEE Conference on
Conference_Location :
London
Type :
conf
DOI :
10.1109/CIFEr.2014.6924059
Filename :
6924059
Link To Document :
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