A New Scenario Reduction Method Based on Higher-Order Moments

Abstract

Scenario reduction is an effective method to ease the computational burden of stochastic programming problems, which aims at choosing a subset of scenarios that can better represent a large number of possible scenarios. Higher-order moments are critical in the scenario reduction process, especially for stochastic programming problems that are greatly affected by the moments. From this idea, we construct a mixed integer linear programming model to improve the reduction accuracy of traditional methods by minimizing the moments’ information loss between the original and reduced scenarios. An improved Benders decomposition algorithm is then designed to find an optimal solution for the model. Finally, the resulting scenarios are examined on an international portfolio selection problem. Empirical and comparative studies are also carried out to reveal the superiority of our proposed scenario reduction method over other existing approaches or models, together with the superior performance of the algorithm. Summary of Contribution: To effectively solve stochastic programming problems, the scenario reduction method has become an active research area to strike a balance between the fine representation of random variables and computational complexity. Thus, how to design a reasonable optimal scenario reduction model and effectively solve this complex model is very important and meaningful. On the other hand, for some stochastic programming problems, especially the portfolio selection problems, statistical properties of risky assets returns may play a more important role in the scenario reduction process. However, the traditional scenario reduction methods have ignored this point. Thus, in this paper, we propose a mixed integer linear programming model to improve the reduction accuracy by minimizing the higher-order moments’ information loss between the original and reduced scenarios. Furthermore, an accelerated Benders decomposition algorithm is also designed to solve the proposed model. Hence, the aim of this paper is to extend the existing scenario reduction method in substantial and meaningful ways.