摘要
基于Markowitz现代投资组合理论,本文首先将证券收益定义为随机模糊变量,用其方差来度量投资组合的风险。在考虑不同交易成本和估计误差存在的情况下,本文提出了具有权重范数约束的最小方差随机模糊投资组合模型,并在其目标函数中引入范数交易成本。基于随机模糊理论,将上述模型转化为具有线性等式和非线性不等式约束的二次规划问题,采用旋转算法进行求解。最后,运用滚动实际数据的方法,比较上述模型的Sharpe Ratio以验证其有效性。
Based on Markowitz’s modern portfolio theory,in this paper,we first define security return as a random fuzzy variable,and use its variance to measure the risk of portfolio.Considering different transaction costs and the existence of estimation errors,we propose weight-norm-constrained random fuzzy minimum variance portfolio model,whose objective function introduces norm transaction costs.According to random fuzzy theories,the model is transformed into a quadratic programming problem with linear equalities and nonlinear inequalities constraints,and then using pivoting algorithm to find the solution.Finally,based on a"rolling empirical data"approach,we compare the Sharp ratio of the proposed models to illustrate the validity of the models.
作者
张鹏
黄梅雨
ZHANG Peng;HUANG Mei-yu(School of Economics&Management,South China Normal University,Guangzhou 510006,China;School of Management,Huazhong University of Science&Technology,Wuhan 430074,China)
出处
《模糊系统与数学》
北大核心
2020年第5期65-76,共12页
Fuzzy Systems and Mathematics
基金
广东省软科学项目(2018A070712030
2019A101002052
2019A101002066)
广东省社科项目(GD19CGL32)
国家自然科学基金资助项目(71271161)
关键词
不确定性建模
权重范数约束
P-范数交易成本
最小方差模型
旋转算法
Uncertainty Modelling
Weight Norm Constraints
P-norm Transaction Costs
Minimum-variance Portfolio Optimization Model
Pivoting Algorithm