波动率能解决投资组合的简单难题吗？
Can Volatility Solve the Naive Portfolio Puzzle?
摘要：
我们研究复杂的波动率估计是否提高了样本外的均值-方差投资组合策略相对于天真的1/N策略的性能。投资组合策略完全依赖于二阶矩。使用跨多个数据集的不同计量经济学和投资组合模型，大多数模型实现了更高的夏普比率和更低的投资组合波动率，即使在控制了周转成本之后，相对于朴素规则，这在统计学和经济学上都是显著的。我们的研究结果表明，采用比样本协方差矩阵更复杂的计量经济学模型的好处，并且均值-方差策略在多个数据集和评估标准中的表现往往优于朴素投资组合。
英文摘要：
We investigate whether sophisticated volatility estimation improves the out-of-sample performance of mean-variance portfolio strategies relative to the naive 1/N strategy. The portfolio strategies rely solely upon second moments. Using a diverse group of econometric and portfolio models across multiple datasets, most models achieve higher Sharpe ratios and lower portfolio volatility that are statistically and economically significant relative to the naive rule, even after controlling for turnover costs. Our results suggest benefits to employing more sophisticated econometric models than the sample covariance matrix, and that mean-variance strategies often outperform the naive portfolio across multiple datasets and assessment criteria.
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