A Self-Organizing State Space Type Microstructure Model for Financial Asset Allocation

Abstract

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.

Keywords

• Asset allocation
• financial markets
• market microstructure model
• Monte Carlo particle filter
• self-organizing state space model