# Central Limit Theorems of Local Polynomial Threshold Estimators for Diffusion Processes with Jumps

## TL;DR

This paper establishes central limit theorems for local polynomial threshold estimators of diffusion process volatility with jumps, addressing challenges in statistical inference due to the estimator's structure.

## Contribution

It provides a novel proof for the CLTs of local polynomial threshold estimators, especially in the local linear case, for diffusion processes with jumps.

## Key findings

- Proved CLTs for local polynomial threshold estimators with jumps.
- Addressed the breakdown of classical CLTs due to estimator structure.
- Introduced a new proof method for these estimators.

## Abstract

Central limit theorems play an important role in the study of statistical inference for stochastic processes. However, when the nonparametric local polynomial threshold estimator, especially local linear case, is employed to estimate the diffusion coefficients of diffusion processes, the adaptive and predictable structure of the estimator conditionally on the $\sigma-$field generated by diffusion processes is destroyed, the classical central limit theorem for martingale difference sequences can not work. In this paper, we proved the central limit theorems of local polynomial threshold estimators for the volatility function in diffusion processes with jumps. We believe that our proof for local polynomial threshold estimators provides a new method in this fields, especially local linear case.