Dynamic assets allocation based on market microstructure model with variable-intensity jumps

QIN Ye-mei(覃业梅)^{1, 2}, PENG Hui(彭辉)^{1, 2}

(1. School of Information Science and Engineering, Central South University, Changsha 410083, China;
2. Hunan Engineering Laboratory for Advanced Control and Intelligent Automation,
Central South University, Changsha 410083, China)

Abstract:In order to characterize large fluctuations of the financial markets and optimize financial portfolio, a new dynamic asset control strategy was proposed in this work. Firstly, a random process item with variable jump intensity was introduced to the existing discrete microstructure model to denote large price fluctuations. The nonparametric method of LEE was used for detecting jumps. Further, the extended Kalman filter and the maximum likelihood method were applied to discrete microstructure modeling and the estimation of two market potential variables: market excess demand and liquidity. At last, based on the estimated variables, an assets allocation strategy using evolutionary algorithm was designed to control the weight of each asset dynamically. Case studies on IBM Stock show that jumps with variable intensity are detected successfully, and the assets allocation strategy may effectively keep the total assets growth or prevent assets loss at the stochastic financial market.

Key words:discrete microstructure model (DMSM); variable jump intensity; evolutionary algorithm (EA); asset allocation; excess demand; market liquidity