Chance-constrained Programming Model for Portfolio Selection in Uncertain Environment

The purpose of this paper is to solve the portfolio problem when security returns are uncertain variables. Two types of portfolio selection programming models based on uncertain measure are provided according to uncertain theory. Since the proposed optimization problems are generally difficult to solve by conventional methods, the models are converted to their crisp equivalents when the return rates are adopted some special uncertain variables such as linear uncertain variable, trapezoidal uncertain variable and normal uncertain variable. Thus the transformed models can be completed by the conventional methods. In the end of the paper, one numerical experiment is provided to illustrate the effectiveness of the method.