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This paper discusses optimal dynamic investment policies for investors, who make the investment decisions in each of the asset categories over time. We construct the framework integrating stochastic optimization and Monte Carlo simulation for dynamic asset allocation, and we propose the linear programming models using simulated paths to solve a large-scale problem in practice. Linear programming models can be formulated to adopt either a åxed-value rule or a åxed-amount rule instead of the general åxed-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 the trade-oã between risk and expected wealth, and we can get interesting results for the dynamic asset allocation policies.

Multi-Period Stochastic Programming Models for Dynamic Asset Allocation