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Fireworks algorithm for mean-VaR/CVaR models

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  • Zhang, Tingting
  • Liu, Zhifeng

Abstract

Intelligent algorithms have been widely applied to portfolio optimization problems. In this paper, we introduce a novel intelligent algorithm, named fireworks algorithm, to solve the mean-VaR/CVaR model for the first time. The results show that, compared with the classical genetic algorithm, fireworks algorithm not only improves the optimization accuracy and the optimization speed, but also makes the optimal solution more stable. We repeat our experiments at different confidence levels and different degrees of risk aversion, and the results are robust. It suggests that fireworks algorithm has more advantages than genetic algorithm in solving the portfolio optimization problem, and it is feasible and promising to apply it into this field.

Suggested Citation

  • Zhang, Tingting & Liu, Zhifeng, 2017. "Fireworks algorithm for mean-VaR/CVaR models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 1-8.
    • Handle: RePEc:eee:phsmap:v:483:y:2017:i:c:p:1-8
    DOI: 10.1016/j.physa.2017.04.036
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    References listed on IDEAS

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    1. Doerner, K.F. & Gutjahr, W.J. & Hartl, R.F. & Strauss, C. & Stummer, C., 2006. "Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection," European Journal of Operational Research, Elsevier, vol. 171(3), pages 830-841, June.
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    Cited by:

    1. Dai, Zhifeng & Wang, Fei, 2019. "Sparse and robust mean–variance portfolio optimization problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1371-1378.
    2. Wu, Jiekang & Wu, Zhijiang & Wu, Fan & Tang, Huiling & Mao, Xiaoming, 2018. "CVaR risk-based optimization framework for renewable energy management in distribution systems with DGs and EVs," Energy, Elsevier, vol. 143(C), pages 323-336.
    3. Gong, Xu & Lin, Boqiang, 2019. "Modeling stock market volatility using new HAR-type models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 194-211.
    4. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
    5. Xie, Nan & Wang, Zongrun & Chen, Sicen & Gong, Xu, 2019. "Forecasting downside risk in China’s stock market based on high-frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 530-541.
    6. Cerqueti, Roy & Giacalone, Massimiliano & Panarello, Demetrio, 2019. "A Generalized Error Distribution Copula-based method for portfolios risk assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 687-695.

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