基于离散近似迭代法的多阶段M-V投资组合优化

提出了离散近似迭代方法,并用该方法求解具有交易成本和交易量限制的多阶段均值-方差(M-V)投资组合模型.离散近似迭代方法的基本思路为:首先,将连续型状态变量离散化,根据网络图的构造方法将上述模型转化多阶段赋权有向图;其次,运用嘉量原理求出起点至终点的最长路程,即获得模型的一个可行解;最后,以该可行解为基础,继续迭代直到前后两个可行解非常接近.还证明了该方法的收敛性和复杂性.

The Discrete Approximate Iteration Method on the Mean-Variance Multiperiod Portfolio Selection

The paper proposes the discrete approximate iteration method,and uses it to solve the multiperiod mean-variance portfolio selection model with the transaction costs and the constraints on trade volumes.Firstly,according to the network method,discretizes the state variables and transforms the model into multiperiod weighted digraph;Secondly,uses Jar-metric principle to solve the maximal path that is the admissible solution;At last,continues iterating until the two admissible solution is near based on the admissible solution.The paper also proves the convergence and complex of the method.