A Hybrid Parameter Estimation for Multi-asset Modeling and Dynamic Allocation Based on Financial Market Microstructure Model

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.