Risk Reduction and Mean-Variance Analysis: An Empirical Investigation

J. Fletcher
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引用次数: 12

Abstract

I examine the performance of global minimum variance (GMV) and minimum tracking error variance (TEV) portfolios in UK stock returns using different models of the covariance matrix. I find that both GMV and TEV portfolios deliver portfolio risk reduction benefits in terms of significantly lower volatility and tracking error volatility relative to passive benchmarks for every model of the covariance matrix used. However, the GMV (TEV) portfolios do not provide significantly superior Sharpe (1966) (adjusted Sharpe) performance relative to passive benchmarks except for the restricted GMV portfolios. I find that a number of alternative covariance matrix models can improve the performance of the restricted TEV portfolio formed using the sample covariance matrix but not the restricted GMV portfolio. I also find that simpler covariance matrix models perform as well as the more sophisticated models. Copyright (c) 2009 The Author Journal compilation (c) 2009 Blackwell Publishing Ltd.
风险降低与均方差分析:实证研究
我使用协方差矩阵的不同模型检验了全球最小方差(GMV)和最小跟踪误差方差(TEV)投资组合在英国股票回报中的表现。我发现GMV和TEV投资组合都提供了投资组合风险降低的好处,因为相对于所使用的协方差矩阵的每个模型的被动基准,GMV和TEV投资组合的波动性和跟踪误差波动性显著降低。然而,GMV (TEV)投资组合并没有提供明显优于被动基准夏普(1966)(调整夏普)的表现,除了受限制的GMV投资组合。我发现一些可选的协方差矩阵模型可以改善使用样本协方差矩阵而不是限制性GMV组合形成的受限TEV组合的性能。我还发现,更简单的协方差矩阵模型表现得和更复杂的模型一样好。版权所有(c) 2009作者期刊编辑(c) 2009布莱克威尔出版有限公司。
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