A Self-Organizing State Space Type Microstructure Model for Financial Asset Allocation
A Self-Organizing State Space Type Microstructure Model for Financial Asset Allocation
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DOI：
10.1109/access.2016.2626720
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发表时间：
2016-11
期刊：
影响因子：
3.9
通讯作者：
Min Gan;Long Chen;Chun-Yang Zhang;Hui Peng
中科院分区：
文献类型：
--
作者：
Min Gan;Long Chen;Chun-Yang Zhang;Hui Peng
在描述金融市场动态行为的模型中，离散时间微观结构模型因其有效地考虑了市场价格、过剩需求和流动性之间的关系而脱颖而出。然而，由于该模型本质上是一个非线性状态空间模型，因此该模型的估计问题具有挑战性。一种较好的解决方案是通过将未知参数和原始模型的状态向量组合成一个新的状态向量来定义一个自组织状态空间模型。然后，用序贯蒙特卡罗方法同时估计系统的参数和状态。为了解决自组织状态空间模型参数初始分布设置困难的问题，我们提出将卡尔曼滤波得到的结果应用于原始微观结构模型。最后，利用自组织状态空间模型设计了基于估计过剩需求的动态资产配置策略。通过中国深圳证券交易所综合指数时间序列对该方法进行了评价，结果表明了该方法的有效性。
Among the models that describe the dynamic behaviors of financial market, the discrete time microstructure model stands out because of its efficiency in considering the relationship between the price, excess demand, and liquidity of a market. However, the estimation problem of such a microstructure model is challenging, because the model is essentially a nonlinear state space model. A decent solution is to define a self-organizing state-space model by combining the unknown parameters and the state vector of the original model into a new state vector. Then, the sequential Monte Carlo method can be used to simultaneously estimate the parameters and states. To handle the difficulty in setting the initial distributions of parameters for the self-organizing state space model, we propose to use the results obtained by the Kalman filter on the original microstructure model. Finally, a dynamic asset allocation strategy is designed based on estimated excess demand using the self-organizing state space model. The proposed methodology is evaluated by the China SZSE (ShenZhen Stock Exchange) Composite Index time series, and the results show its effectiveness.