| Costa和Paiva(2002)认为TEV优化问题和EV优化问题都存在模型输入参数的微小变化会引起解系数的较大变化问题。为了提高鲁棒性，研究提出了将跟踪优化问题归纳为线性矩阵不等式问题求解。PhilippeJorion(2002)指出股票型基金的系统风险高于基准指数，可以运用衍生工具的杠杆效应构造增强型指数基金（Enhancedindexfunds），该指数基金可以创造出不低于基准指数的收益率，同时也能够很好的控制跟踪误差。LiuYungang,ZhangJifeng(2003)运用随机非线性系统的满意输出反馈控制（satisfacitonoutputfeedbackcontrols）方法对风险敏感型指数化的投资组合的误差进行跟踪优化。他使用二次函数表示指数的收益率，运用积分反推法（integratorbackteppingmethod）和建设性的输出反馈控制(outputfeedbackcontrol)设计，使得敏感性指数化的投资组合的收益率高于既定值。SergioM.Focardi;FrankJ.Fabozzi(2004)在JobsonandKorkie（1980）、BestandGrauer（1992）等学者的影响下，也发现相关系数主要反映收益率在短期内的变化趋势，长期的均衡关系可以通过协整理论来度量；同时他还发现运用协整理论来度量跟踪绩效，不需要重复和频繁的调整投资组合中各成分股的持有比例，因此不论是在长期还是短期都具有较好的效果。R.TyrrellRockafellar,StanislavUryasev(2002)采用CVaR作为风险度量模型，建立了跟踪误差最小化的指数组合优化模型，并同时以纽约证券交易所和香港证券交易所的股票为样本，给出了跟踪误差最小时的样本内和样本外的最优投资策略，对CVaR控制风险的有效性进行了市政检验。DunisChristianL,HoRichard(2005)进一步使用道琼斯指数实证研究发现跟踪指数组合与基准指数具有稳定的协整关系；同时采用长期或短期的市场中立策略，在不利的市场环境中也能够产生稳定的回报。ChristianDose,andSilvanoGindotti(2005)在随机优化技术的框架，运用时间序列聚类分析的方式，描述了指数跟踪和增强型指数跟踪问题。AndreasLindemann,ChristianL.Dunis,PauloLisboa(2005）运用高斯混合神经网络模型（Gaussianmixtureneuralnetwork）估计摩根士丹利技术指数追踪基金（MorganStanleyTechnologyIndexTrackingFund，MTK）的概率密度函数，同时将传统的预测模型移动平均收敛发散（movingaverageconvergencedivergence,MACD）模型、自回归移动平均模型(autoregressivemovingaveragemodel,ARMA)、逻辑回归模型（logisticregressionmodel）和多层感知器网络（Multi-layerperceptronnetwork,MLP）以及高斯混合神经网络模型对于追踪模型的收益率的预测效果进行比较实证研究，研究发现两个网络模型的预测效果均由于基准指数收益率，而高斯混合神经网络模型的预测效果最好。LianYu,ShuzhongZhang,andXunYuZhou(2006)在卖空以及下行概率的约束下使用高阶矩来度量风险，并运用库恩-塔克条件解决金融指数的最优跟踪问题。FrancescoCorielli,MassimilianoMarcellino(2006)采用动态的因子模型对影响跟踪误差的要素进行回归分析，研究了影响EuroStoxx50指数的跟踪能力和跟踪误差的影响因素。DavidD.Yao,ShuzhongZhang,XunYuZhou(2006)在随机线性二次控制的框架下，运用半定规划生成最优反馈控制矩阵来解决基于收益率的跟踪误差最小化问题的动态最优值的求解。
随着我国资本市场的不断完善和成熟，指数化投资策略必将得到广泛的研究和应用。因此如何将国外的研究成果应用于适合我国资本市场的指数化投资管理中，将是我国学者进一步研究和探讨的课题。
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