Norm Constrained Empirical Portfolio Optimization with Stochastic Dominance: Robust Optimization Non-Asymptotics

Stelios Arvanitis ()
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Stelios Arvanitis: Department of Economics, AUEB

No 1533, Working Paper from Economics Department, Queen's University

Abstract: The present note provides an initial theoretical explanation of the way norm regularizations may provide a means of controlling the non-asymptotic probability of False Dominance classification for empirically optimal portfolios satisfying empirical Stochastic Dominance restrictions in an iid setting. It does so via a dual characterization of the norm-constrained problem, as a problem of Distributional Robust Optimization. This enables the use of concentration inequalities involving the Wasserstein distance from the empirical distribution, to obtain an upper bound for the non-asymptotic probability of False Dominance classification. This leads to information about the minimal sample size required for this probability to be dominated by a predetermined significance level.

Keywords: Portfolio optimization; Stochastic dominance; â„“p regularization; Wasserstein distance; Distributionally robust optimization; Concentration inequality; False dominance classification (search for similar items in EconPapers)
JEL-codes: C44 C58 G11 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2025-02
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