Multi-period stochastic programming models using simulation paths for strategic asset allocation

This paper discusses optimal dynamic investment policies for investors, who make investment decisions in each of the asset categories over time. We propose linear programming models using simulated paths to solve large-scale problems in practice. Linear programming models can be formulated to adopt either fixed-value rule or fixed-amount rule instead of general fixed-proportion rule. These formulations can be simply implemented and solved very fast. Some numerical examples are tested to illustrate the characteristics of the models. These models can be used to improve trade-off between risk and expected wealth, and we can get interesting results for dynamic asset allocation policies.