Can Volatility Solve the Naive Portfolio Puzzle?

Michael Curran (), Patrick O'Sullivan () and Ryan Zalla ()
Additional contact information
Patrick O'Sullivan: Schroders Investment Management, 1 London Wall Place, London, UK., https://www.schroders.com/en/uk/adviser/
Ryan Zalla: Economics Department, University of Pennsylvania, 133 South 36th Street, Philadelphia, PA 19104, USA., https://economics.sas.upenn.edu/people/ryan-zalla

No 52, Villanova School of Business Department of Economics and Statistics Working Paper Series from Villanova School of Business Department of Economics and Statistics

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 out-perform the naive portfolio across multiple datasets and assessment criteria.

Keywords: mean-variance; naive portfolio; volatility (search for similar items in EconPapers)
JEL-codes: G11 G17 (search for similar items in EconPapers)
Date: 2022-02
New Economics Papers: this item is included in nep-ban, nep-cwa, nep-fmk, nep-ore and nep-rmg
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Working Paper: Can Volatility Solve the Naive Portfolio Puzzle? (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:vil:papers:52

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