Can volatility solve the naive portfolio puzzle?

Michael Curran, Patrick O'Sullivan and Ryan Zalla

Quantitative Finance, 2023, vol. 23, issue 11, 1545-1560

Abstract: 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 portfolios and econometric 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:23:y:2023:i:11:p:1545-1560

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DOI: 10.1080/14697688.2023.2249996

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