High-Order Portfolio Optimization Problem with Background Risk

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.