We investigate whether sophisticated volatility estimation improves the out-of-sample performance of mean-variance portfolio strategies relative to the na\"{\i 1/N strategy. The portfolio strategies rely solely upon second moments. Using a diverse group of econometric and portfolio models across multiple datasets, we show that a majority of models achieve significantly higher Sharpe ratios and lower portfolio volatility  relative to the na\"{\i rule, even after controlling for turnover costs. Our results suggest that there are benefits to employing more sophisticated econometric models than the sample covariance matrix, and that mean-variance strategies often outperform the na\"{\i portfolio across multiple datasets and assessment criteria.
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.
R
na\"{\i
naive
R
we show that a majority of models achieve significantly
most models achieve
A
that are statistically and economically significant
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na\"{\i
naive
D
that there are
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na\"{\i
naive