Optimal Portfolio Choice with Estimation Risk: No Risk-Free Asset Case

Raymond Kan (), Xiaolu Wang () and Guofu Zhou ()
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Raymond Kan: Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada
Xiaolu Wang: Ivy College of Business, Iowa State University, Ames, Iowa 50011
Guofu Zhou: Olin School of Business, Washington University in St. Louis, St. Louis, Missouri 63130; CAFR, Shanghai 200030, China

Management Science, 2022, vol. 68, issue 3, 2047-2068

Abstract: We propose an optimal combining strategy to mitigate estimation risk for the popular mean-variance portfolio choice problem in the case without a risk-free asset. We find that our strategy performs well in general, and it can be applied to known estimated rules and the resulting new rules outperform the original ones. We further obtain the exact distribution of the out-of-sample returns and explicit expressions of the expected out-of-sample utilities of the combining strategy, providing not only a fast and accurate way of evaluating the performance, but also analytical insights into the portfolio construction.

Keywords: portfolio choice; estimation risk; mean-variance optimization; optimal combining (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:3:p:2047-2068

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