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arXiv 2106.10491q-fin.PM
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分布误设定下均值-方差投资组合规则的有效前沿

The efficient frontiers of mean-variance portfolio rules under distribution misspecification

Andrew Paskaramoorthy, Tim Gebbie, Terence van Zyl

备注：
8 pages, 2 figures, 4 tables, submitted to Fusion2021
发表信息：
2021 IEEE 24th International Conference on Information Fusion (FUSION), 2021, pp. 1-8
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摘要：

结合预测和优化的均值-方差投资组合决策已被证明具有较差的经验表现。在这里，我们考虑各种收缩方法的性能，在不同的分布假设下，通过它们的有效前沿来研究合理偏离正态性的影响。也就是说，我们调查的影响，一阶自相关，二阶自相关，偏度，和过剩峰度。我们表明，收缩方法倾向于重新缩放样本有效前沿，这可以根据正态性的局部扰动的性质而改变。这种重新调整意味着，标准的方法比较决策规则的固定水平的风险厌恶是有问题的，更何况在一个动态的市场环境。我们的研究结果表明，比较有效前沿有严重的影响，反对文献中的主流思想。也就是说，样本估计量优于均值的Stein型估计量，并且改进协方差的预测比改进均值的预测更重要。

英文摘要：

Mean-variance portfolio decisions that combine prediction and optimisation have been shown to have poor empirical performance. Here, we consider the performance of various shrinkage methods by their efficient frontiers under different distributional assumptions to study the impact of reasonable departures from Normality. Namely, we investigate the impact of first-order auto-correlation, second-order auto-correlation, skewness, and excess kurtosis. We show that the shrinkage methods tend to re-scale the sample efficient frontier, which can change based on the nature of local perturbations from Normality. This re-scaling implies that the standard approach of comparing decision rules for a fixed level of risk aversion is problematic, and more so in a dynamic market setting. Our results suggest that comparing efficient frontiers has serious implications which oppose the prevailing thinking in the literature. Namely, that sample estimators out-perform Stein type estimators of the mean, and that improving the prediction of the covariance has greater importance than improving that of the means.

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