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Guo, X., Chan, R. H., Wong, W. K., & Zhu, L. (2019). Mean-Variance, Mean-VaR, Mean-CVaR Models for Portfolio Selection with Background Risk. Risk Management, 21, 73-98.

has been cited by the following article:

• TITLE: High-Order Portfolio Optimization Problem with Background Risk

  AUTHORS: Xiaolu Zhou

  KEYWORDS: Background Risk, Higher Moment, Genetic Algorithm

  JOURNAL NAME: Open Journal of Business and Management, Vol.9 No.3, April 2, 2021

  ABSTRACT: After Markowitz proposed the mean-variance model, the research on portfolio problems has been a hot topic for many investors. The research on portfolio optimization is becoming more and more perfect. The investment theory changes from second-order moment to high-order moment, and from single-stage to multi-stage. More and more factors affecting portfolio optimization are taken into consideration. In this paper, a high-order portfolio optimization problem considering background risks is studied. Firstly, an optimization model of high-order moments including background risks is established, and the genetic algorithm is used to solve the model. Finally, the effects of background risks and high-order moments on the portfolio optimization model are analyzed empirically.