  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.
