Optimal Programming Models for Portfolio Selection with Uncertain Chance Constraint

  •  Limei Yan    


The paper is concerned with the portfolio selection problem about how to assign one’s money in security market in order to obtain the maximal profit. One type expected maximization programming model with chance constraint in which the security returns are uncertain variables are proposed in accordance with uncertainty theory. Since the provided models can not be solved by the traditional methods, the crisp equivalents of the corresponding models are discussed when the uncertain returns are chosen as some special cases such as linear uncertain variables, trapezoidal uncertain variables and normal uncertain variables. Two numerical examples with different types of uncertain variables are given in order to demonstrate the effectiveness and feasibility of the proposed programming models. Finally, the paper gives the conclusion.

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