在用方差控制投资组合风险的同时,由于方差的对称性导致投资组合的收益也受到限制.相比之下,下偏距(10werpartialmoment:LPM)由于具有只控制风险,而不限制收益的特点,在近年来倍受关注.但在非正态假设下,LPM无法获得良好的解析性质.在对资产收益分布未知的假设下,通过使用最坏情形下的LPM来度量投资组合的损失,提出了具有多元权值约束的鲁棒积极投资组合问题,并获得了具有m(m=0,i,2)-阶LPM约束的鲁棒积极投资组合问题的解析解.通过分析解的性质和比较问题的有效前沿,得到了许多有趣的和新颖的结果.数值结果比较表明,鲁棒LPM模型比经典的均值一方差模型具有许多更好的性能.
The return of portfolios will be limited when one uses the variance of portfolio to control risk. Com- pared with the shortcoming of the variance, lower partial moment (LPM) is paid close attention by many re- searchers due to the fact that LPM controls only the risk of portfolios without limiting the return. But, under the assumption of non-normal distribution, one can not generally obtain the analytic properties of LPM models. Motivated by these facts, under the assumption of uncertainty distribution, we propose a class of robust track- ing error portfolio selection problems with multiple weights constraints in which we use the worst-case lower partial moment (LPM) to measure the loss of portfolios. The analytic solutions of the proposed robust models with-order LPM constraints are obtained. Some interesting and novel results are found based on the geometrical presentations of efficient frontiers. The numerical comparisons indicate that the proposed robust models have much better performance than the classical mean-variance model.