Multi-period stochastic optimization models for dynamic asset allocation, J.Banking Finance

Asset allocation decisions are critical for investors with diversiåed portfolios. Institutional in-vestors must manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. In order to determine the asset mix explicitly, one may use a multi-period portfolio optimization model. The concept of scenarios is typically employed for modeling random parameters in multi-period stochastic programming (MSP) models, and scenarios are constructed via a tree structure. Another approach for developing dynamic investment strategies, which oãers an alternative to stochastic programming, is the dynamic stochastic control. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki (2001b, 2001c). Hibiki(2003) developed the general formulation for several investment strategies, and highlight its features and properties using some numerical tests. Scenarios are constructed via a simulated path structure. The advantage of simulated paths comparing to scenario trees is higher accuracy of description of uncertainties associated with asset returns. In addition, we can make conditional decisions in this framework similarly to a scenario tree model. This model is called a hybrid model. It can be easily implemented and eéciently solved using sophisticated mathematical programming software. In this paper, we compare two types of multi-period stochastic optimization (MPSO) models, and clarify that the hybrid model can evaluate and control risk better than the scenario tree model using some numerical tests. According to the numerical results, the eécient frontier of the hybrid model with the åxed-proportion strategy dominates that of the scenario tree model when we evaluate them on the simulated paths. Moreover, the optimal solutions of the hybrid model is more appropriate than those of the scenario tree model, which are very extream ones.