Error-order conjecture for sample optimal portfolio weights

From papers

Let Σ\Sigma be the true covariance matrix, with eigenvalues σk2\sigma_k^2, and let ww^* and w^\hat{w}^* denote the true and sample optimal portfolio weights, respectively. For each coordinate kk, write w^k\hat{w}^*_k and wkw^*_k for their kk-th entries.

Error-order conjecture. The expected coordinatewise error satisfies

Ew^kwk=O(σkpn),1kn.\operatorname{\mathbb{E}}\left|\hat{w}^*_k-w^*_k\right|=O\left(\sigma_k\sqrt{\frac{p}{n}}\right),\qquad \forall\,1\leq k\leq n.

The constant in the order depends on the smallest and largest eigenvalues of Σ\Sigma.

This conjecture summarizes the paper's experimental observations about the accuracy of sample optimal portfolio weights as the dimension-to-sample-size ratio changes. The supplied text does not establish the estimate theoretically or indicate whether it has been resolved.

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Sources & referencesView supporting material

Primary source

JunTao Duan and Ionel Popescu, “LoCoV: low dimension covariance voting algorithm for portfolio optimization”, arXiv:2204.00204 (2022).

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