Semiparametric method for detecting multiple change points model in financial time series

The problem of multiple change points has been discussed in these years on the background of financial shocks. In order to decrease the damage, it is worthy to find a more available model for the problem as precise as possible by the information from data set. This paper proposes the problem of detecting the change points by semiparametric test. The change points estimations are obtained by empirical likelihood method. Then some asymptotic results for multiple change points are obtained by loglikelihood ratio test and law of large numbers. Furthermore, the consistency of change points estimations is presented. Indeed, the method and steps to find the change points are derived. The simulation experiments prove that the semiparametric test is more efficient than nonparametric test. The diagnosis with simulation and the applications for multiple change points also illustrates the proposed model well.