A modeling approach to financial time series based on market microstructure model with jumps
A continuous-time generalized market microstructure (GMMS) model and its discretized model are proposed for characterizing a class of financial time series. The GMMS model is a kind of jump-diffusion model that may describe the dynamic behaviors of measurable market price, immeasurable market excess demand and market liquidity, as well as the interaction among the three variates in a market. The model includes a jump component that is used to capture the large abnormal variations of financial assets, which may occur when a market is affected by some special events happened suddenly, such as release of important financial information. On the basis of the discrete-time GMMS model, an online recursive jump detection algorithm is proposed, which is developed in accordance with the Markov property of financial time series and the Bayes theorem. Simulations and case studies demonstrate the feasibility and effectiveness of the model and its estimation approach presented in this paper.
financial time series microstructure modeling jump-diffusion model jump detection extended kalman filter maximum likelihood estimation