  In this paper, robust nonparametric estimators, instead of local linear
estimators, are adapted for infinitesimal coefficients associated with
integrated jump-diffusion models to avoid the impact of outliers on accuracy.
Furthermore, consider the complexity of iteration of the solution for local
M-estimator, we propose the one-step local M-estimators to release the
computation burden. Under appropriate regularity conditions, we prove that
one-step local M-estimators and the fully iterative M-estimators have the same
performance in consistency and asymptotic normality. Through simulation, our
method present advantages in bias reduction, robustness and reducing
computation cost. In addition, the estimators are illustrated empirically
through stock index under different sampling frequency.
