Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?
Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?
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DOI：
10.1093/rfs/hhm075
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发表时间：
2009-05-01
影响因子：
8.2
通讯作者：
Uppal, Raman
中科院分区：
文献类型：
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作者：
DeMiguel, Victor;Garlappi, Lorenzo;Uppal, Raman
我们评估了基于样本的均值-方差模型的样本外性能，以及它旨在减少估计误差的扩展，相对于朴素的1/N投资组合。在我们通过七个经验数据集评估的14个模型中，没有一个模型在夏普比率、确定性等价回报或周转率方面始终优于1/N规则，这表明，在样本之外，最优多元化的收益超过了估计误差。基于美国股市校准的参数，我们的分析结果和模拟表明，基于样本的均值-方差策略及其扩展所需的估计窗口对于拥有25个资产的投资组合约为3000个月，对于拥有50个资产的投资组合约为6000个月。这表明，在最优投资组合选择承诺的收益在样本之外真正实现之前，仍有许多“英里要走”。
We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1/N portfolio. Of the 14 models we evaluate across seven empirical datasets, none is consistently better than the 1/N rule in terms of Sharpe ratio, certainty-equivalent return, or turnover, which indicates that, out of sample, the gain from optimal diversification is more than offset by estimation error. Based on parameters calibrated to the US equity market, our analytical results and simulations show that the estimation window needed for the sample-based mean-variance strategy and its extensions to outperform the 1/N benchmark is around 3000 months for a portfolio with 25 assets and about 6000 months for a portfolio with 50 assets. This suggests that there are still many "miles to go" before the gains promised by optimal portfolio choice can actually be realized out of sample.