@article{Chen2025, 
author = {Zhongyu Chen and Juliang Yin},
title = {Detecting jumps in stochastic volatility jump-diffusion models via the power variation approach},
year = {2025},
journal = {AIMS Mathematics},
volume = {10},
number = {8},
pages = {19189-19216},
keywords = {high-frequency data, jump intensity, long time span jump test, power variation},
url = {https://www.sciopen.com/article/10.3934/math.2025858},
doi = {10.3934/math.2025858},
abstract = {It is well-known that pretesting the presence of the jump component in an underlying price process is crucial for modeling this process. In this paper, we propose a consistent test for jump intensity of the conditional Poisson process in a stochastic volatility jump diffusion model. Theoretically, we derive the infill and long-span asymptotic properties of realized power variation under some suitable conditions, and verify the asymptotic size and power of the proposed test. Furthermore, the finite-sample performance of our proposed test is illustrated through simulation analysis, and an application to real price series provides empirical evidence of significant jump intensities.}
}