• 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