# Nonparametric inference of gradual changes in the jump behaviour of time-continuous processes

## TL;DR

This paper introduces a new statistical method to detect and localize gradual changes in the jump behavior of continuous-time stochastic processes, using a novel measure and bootstrap techniques for uncertainty quantification.

## Contribution

It proposes a new measure for gradual change detection in jump behavior and establishes weak convergence results with a bootstrap method for inference.

## Key findings

- New measure for time variation in jump behavior
- Weak convergence of empirical tail integral process
- Bootstrap procedure for statistical uncertainty

## Abstract

In applications the properties of a stochastic feature often change gradually rather than abruptly, that is: after a constant phase for some time they slowly start to vary. In this paper we discuss statistical inference for the detection and the localisation of gradual changes in the jump characteristic of a discretely observed Ito semimartingale. We propose a new measure of time variation for the jump behaviour of the process. The statistical uncertainty of a corresponding estimate is analyzed by deriving new results on the weak convergence of a sequential empirical tail integral process and a corresponding multiplier bootstrap procedure.