Mean-variance-skewness Portfolio Selection Model in General Uncertain Environment

The aim of this paper is to develop mean-variance-skewness portfolio selection models in uncertain environment. Here the returns of the securities are assumed to be uncertain variables that cannot be estimated by randomness or fuzziness. The model in uncertain environment is formulated as a non-linear programming model based on uncertain programming approaches proposed by Liu in 2007. Since there is no efficient solution method to solve the proposed model, assuming the returns as some special uncertain variables, the original portfolio selection model is transformed into equivalent deterministic model that can be solved by any state-of-the-art solution methodology. The feasibility and effectiveness of the proposed method is verified by numerical example extracted from Bombay Stock Exchange (BSE). Security returns are considered in the form of triangular uncertain variables. Zimmermann's fuzzy goal programming method is used for solution.