Reconciling mean-variance portfolio theory with non-Gaussian returns
(2020)
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- Abstract
- Mean-variance portfolio theory remains frequently used as investment rationale because of its simplicity, its closed-form solution, and the availability of many well-performing robust estimators. At the same time, it is also frequently rejected on the grounds that it ignores the higher moments of non-Gaussian returns. However, higher-moment portfolios are associated with many different objective functions, are numerically more complex, and exacerbate estimation risk. In this paper, we reconcile mean-variance portfolio theory with non-Gaussian returns by identifying, among all portfolios on the mean-variance efficient frontier, the one that optimizes a chosen higher-moment criterion. Via numerical simulations and an empirical analysis, we find that, for three higher-moment objective functions and adjusting for transaction costs, the resulting portfolios outperform the minimum-variance and fully optimized portfolios out of sample both in terms of Sharpe ratio and higher moments, thus striking a favorable tradeoff between specification and estimation error.
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Citations
Lassance, N. (2020). Reconciling mean-variance portfolio theory with non-Gaussian returns. https://hdl.handle.net/2078.5/118131