Enhancing Mean-Variance Portfolio Selection by Modeling Distributional Asymmetries
Enhancing Mean-Variance Portfolio Selection by Modeling Distributional Asymmetries
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DOI：
10.2139/ssrn.2259073
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发表时间：
2013-01
期刊：
影响因子：
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通讯作者：
Rand Kwong Yew Low;R. Faff;K. Aas
中科院分区：
文献类型：
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作者：
Rand Kwong Yew Low;R. Faff;K. Aas
为什么均值-方差(MV)模型表现如此之差？在寻找这个问题的答案时，我们通过从一个明确包含分布不对称性的多变量概率模型中抽样来估计预期收益。具体地说，我们的实证分析表明，与历史抽样窗口相比，使用包含动态特征(如自回归、波动率聚集性和偏度)的Copula边际模型来减少估计误差。使用这些基于Copula的模型，我们发现一些基于MV的规则在统计上表现出显著的性能改进，即使在考虑了交易成本之后也是如此。然而，我们发现，在考虑交易成本后，表现优于天真的等权(1/N)策略仍然是一项难以捉摸的任务。
Why do mean–variance (MV) models perform so poorly? In searching for an answer to this question, we estimate expected returns by sampling from a multivariate probability model that explicitly incorporates distributional asymmetries. Specifically, our empirical analysis shows that an application of copulas using marginal models that incorporate dynamic features such as autoregression, volatility clustering, and skewness to reduce estimation error in comparison to historical sampling windows. Using these copula-based models, we find that several MV-based rules exhibit statistically significant and superior performance improvements even after accounting for transaction costs. However, we find that outperforming the naive equally-weighted (1/N) strategy after accounting for transactions costs still remains an elusive task.