Confidence interval of the jump activity index based on empirical likelihood using high frequency data

It is widely accepted that jumps exist in the asset price process. The jump activity index is a natural measure of how frequent the jumps are. Statistical inference of the jump activity index is of importance in determining the type of process that underlies the dynamics of the log price process. In this paper, we implement the empirical likelihood approach to construct the confidence interval of the jump activity index of a pure jump model using high frequency data. Wilks' theorem is established. We also extend the result on Zhao and Wu (2009)'s estimator to the more general framework in this paper. Simulation studies demonstrate the good performance of the empirical likelihood approach. Compared with the existing non-parametric estimator proposed by Zhao and Wu (2009), the empirical likelihood approach gives more accurate coverage probabilities in the simulation studies.