Improving portfolio risk profile with threshold accepting

Computational Intelligence for Financial Engineering & Economics(2014)

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摘要
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.
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关键词
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
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