In the previous works, a discrete-time microstructure (DTMS) model for financial market was constructed by using identification technology and was successfully applied to dynamic asset allocation based on the identified excess demand. However, the initial value setting of the parameters has a great influence on the estimated results of the DTMS model, which may make the estimated model to describe the dynamic characteristics of the financial time series poor and also affect the investment results indirectly. To overcome the weakness, this paper proposes a global optimization method which combines particle swarm optimization (PSO) and genetic algorithm (GA) to estimate the initial parameters. In the paper, the multi-asset DTMS model is established, and a multi-asset dynamic allocation strategy based on excess demand obtained from the DTMS model is also designed. Furthermore, the paper also discusses the impact of mutual correlation of assets on portfolio. Case studies show that, when a portfolio is composed of several stocks which are weak correlation, its total return of the portfolio is more than the sum of two-asset allocation for each stock; while the correlation between stocks is high, the obtained total return is not better than those of two-asset allocation.

中文翻译：

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基于金融市场微观结构模型的多资产建模与动态配置混合参数估计

在前人的工作中，利用识别技术构建了金融市场的离散时间微观结构（DTMS）模型，并成功地应用于基于识别出的过剩需求的动态资产配置。但是，参数的初始值设置对DTMS模型的估计结果影响很大，可能使估计模型对金融时间序列动态特性的描述较差，也间接影响投资结果。针对上述不足，本文提出了一种结合粒子群优化（PSO）和遗传算法（GA）的全局优化方法来估计初始参数。文中建立了多资产DTMS模型，并设计了一种基于DTMS模型得到的超额需求的多资产动态配置策略。此外，本文还讨论了资产相互关联对投资组合的影响。案例研究表明，当一个投资组合由多只相关性较弱的股票组成时，该投资组合的总收益大于每只股票两种资产配置的总和；虽然股票之间的相关性很高，但获得的总回报并不比双资产配置好。