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