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Deciphering robust portfolios
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AI
DOI：10.1016/j.jbankfin.2014.04.021.png)
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摘要
En 中文
Robust portfolio optimization has been developed to resolve the high sensitivity to inputs of the Markowitz mean variance model. Although much effort has been put into forming robust portfolios, there have not been many attempts to analyze the characteristics of portfolios formed from robust optimization. We investigate the behavior of robust portfolios by analytically describing how robustness leads to higher dependency on factor movements. Focusing on the robust formulation with an ellipsoidal uncertainty set for expected returns, we show that as the robustness of a portfolio increases, its optimal weights approach the portfolio with variance that is maximally explained by factors. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Robust portfolio optimization
Mean-variance model
Fundamental factors
期刊
J
IF:
3.8
论文数:
6.4K
被引数:
2.4W
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