A Hybrid Parameter Estimation for Multi-asset Modeling and Dynamic Allocation Based on Financial Market Microstructure Model
A Hybrid Parameter Estimation for Multi-asset Modeling and Dynamic Allocation Based on Financial Market Microstructure Model
复制标题
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
DOI：
10.1142/s0218213020400072
复制
发表时间：
2020-11
影响因子：
1.1
通讯作者：
Tan Ping
中科院分区：
文献类型：
--
作者：
Qin Yemei;Zhong Yangyu;Lei Zhen;Peng Hui;Zhou Feng;Tan Ping
在前人的研究中，利用识别技术构建了金融市场离散时间微观结构（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。然而，参数的初始值设置对DTMS模型的估计结果影响较大，可能使估计模型对金融时间序列动态特征的描述较差，也间接影响投资结果。为了克服这一缺点，本文提出了一种结合粒子群算法和遗传算法的全局优化方法来估计初始参数。本文建立了多资产DTMS模型，并设计了基于该模型得到的过剩需求的多资产动态配置策略。此外，本文还讨论了资产之间的相互相关性对投资组合的影响。案例研究表明，当一个投资组合由多只弱相关股票组成时，其投资组合的总收益大于每只股票的两种资产配置之和；当股票之间的相关性较高时，所获得的总收益并不优于双资产配置。
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.