Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/322398 
Year of Publication: 
2025
Series/Report no.: 
Queen’s Economics Department Working Paper No. 1527
Publisher: 
Queen's University, Department of Economics, Kingston (Ontario)
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.
Subjects: 
Portfolio optimization
Stochastic dominance
&#x2113
p regularization
Wasserstein distance
Distributionally robust optimization
Concentration inequality
False dominance classification
JEL: 
C44
C58
G11
Document Type: 
Working Paper

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