Abstract
The paper studies a financial portfolio selection problem under 1st-order stochastic dominance constraints. These constraints constitute lower bounds on the return profile of the portfolio. In particular, they allow searching for a better portfolio than some reference portfolio by comparing their cumulative distribution functions. Candidate objective functions are the average return, a value at risk, or the average value at risk. The optimization problems obtained are computationally hard because of possibly non-convex constraints and possibly discontinuous objective functions. In the case of a discrete distribution of the return, we develop numerical procedures to solve the problem. The proposed approach uses new exact penalty functions to tackle with the 1st-order stochastic dominance constraints. The resulting penalized objective function is further optimized be the stochastic successive smoothing method as a local optimizer within some branch and bound global optimization scheme. The approach is numerically and graphically illustrated on small portfolio selection problems up to dimension 10.
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