A linear model for tracking error minimization
A linear model for tracking error minimization
复制标题
DOI：
10.1016/s0378-4266(98)00076-4
复制
发表时间：
1999-01-01
影响因子：
3.7
通讯作者：
Zimmermann, H
中科院分区：
文献类型：
--
作者：
Rudolf, M;Wolter, HJ;Zimmermann, H
本文研究了四个模型，以最小化投资组合和基准之间的跟踪误差。由于基金经理的业绩报酬是线性的，因此我们可以认为线性离差比平方离差更能准确地描述投资者的风险态度。所有模型的共同点是，绝对偏差最小化，而不是传统优化模型的平方偏差。线性规划被制定为导出明确的解决方案。该模型适用于包含六个国家的股票市场指数（美国，日本，英国，德国，法国，瑞士）的投资组合和跟踪误差相对于MSCI（摩根士丹利资本国际指数）世界股票市场指数最小化。的结果进行了比较的二次跟踪误差优化技术。优化后的投资组合权重及其风险/收益属性在不同的模型中是不同的，这意味着优化模型应该针对特定的投资目标。最后，证明了线性跟踪误差优化等价于期望效用最大化和下偏矩最小化。(C)1999 Elsevier Science B. V.保留所有权利。JEL分类：C63; G11。
This article investigates four models for minimizing the tracking error between the returns of a portfolio and a benchmark. Due to linear performance fees of fund managers, we can argue that linear deviations give a more accurate description of the investors' risk attitude than squared deviations. All models have in common that absolute deviations are minimized instead of squared deviations as is the case for traditional optimization models. Linear programs are formulated to derive explicit solutions. The models are applied to a portfolio containing six national stock market indexes (USA, Japan, UK, Germany, France, Switzerland) and the tracking error with respect to the MSCI (Morgan Stanley Capital International Index) world stock market index is minimized. The results are compared to those of a quadratic tracking error optimization technique. The portfolio weights of the optimized portfolio and its risk/return properties are different across the models which implies that optimization models should be targeted to the specific investment objective. Finally, it is shown that linear tracking error optimization is equivalent to expected utility maximization and lower partial moment minimization. (C) 1999 Elsevier Science B.V. All rights reserved. JEL classification: C63; G11.