Naive versus optimal diversification: Tail risk and performance

Inchang Hwang, Simon Xu and Francis In

European Journal of Operational Research, 2018, vol. 265, issue 1, 372-388

Abstract: It is well documented in portfolio optimization that naive diversification outperforms optimal mean–variance diversification because the latter is subject to severe estimation error. Our study provides an alternative explanation for the outperformance of naive diversification by examining the tail risk of naive diversification relative to optimal mean–variance diversification. We utilize a rolling-sample approach and compare the out-of-sample performance and tail risk of various optimal strategies to that of the naive diversification strategy. Using portfolios consisting of individual stocks, we show that for portfolios containing relatively small number of stocks, naive diversification outperforms optimal mean–variance diversification and is less exposed to tail risk. However, for relatively large number of stocks in the portfolio, naive diversification maintains its superior performance but increases tail risk and results in more concave portfolio returns. These results imply that the outperformance of naive diversification acts as compensation for the increase in tail risk and concavity.

Keywords: Investment analysis; Mean–variance analysis; Portfolio optimization; Tail risk (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:265:y:2018:i:1:p:372-388

DOI: 10.1016/j.ejor.2017.07.066

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