Portfolio Model Based on Scenario Tree

Abstract

The uncertainty of return rate will affect the investment decision. In this paper, the ARMA-GARCH model is used to describe the data characteristics of stock returns, and the Monte Carlo method is used to construct a scenario tree containing the stock return rate and node probability. The decision rules are used to determine the nodes on the scene tree, and two mean-variance models are established based on the scene tree. Finally, four stock data are selected to optimize the portfolio of the constructed model, the results show that the scenario tree has good advantages in describing the uncertainty problem, and the constructed model is effective and feasible; the difference between the two models is analyzed and compared, which provides a reference for different investors.