Stochastic Analysis And Parameters Estimate For Futures Price Based On Particle Filter

This paper focuses on the analysis of stock index futures prices. In the paper, Imodel the future price with two-factor stochastic modeling and estimate state andparameters jointly with particle filter.In modeling the futures price, I take into account the characteristics of the stockindex futures to reflect its short-term fluctuations around the mean line and long-termuncertainty because of the industry policy or the change of economic environmentwith two factors.For the established state-space model, we use the particle filter, rather than thecommonly used Kalman filtering. Kalman filtering is mainly applied to linear Gaussiandynamic system, for the case of non-linear, extended Kalman filter and unscentedKalman filter approximation corresponding adjustments were made, and the case fornon-Gaussian noise, the Kalman filter estimated effect will be reduced. However,particle filter on the state space modeling without any restrictions on the face ofnonlinear non-Gaussian models, particle filter will appear at ease.Finally, this paper through the CSI300stock index as an empirical object to thefutures price to implement joint state and parameter estimation.