Change point inference in ergodic diffusion processes based on high frequency data

Tonaki, Yozo; Uchida, Masayuki

doi:10.1016/j.spa.2022.12.011

Cited by 1 publication

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“…In other words, we do not consider the situation where both the diffusion and drift parameters change. For this situation, see Tonaki and Uchida (2021). This paper treats the change point inference for the diffusion parameter when there is a change in the diffusion parameter.…”

Section: Resultsmentioning

confidence: 99%

Estimation for change point of discretely observed ergodic diffusion processes

Tonaki

Kaino

Uchida

2022

Scandinavian J Statistics

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We treat the change point problem in ergodic diffusion processes from discrete observations. Tonaki et al. (2021a) proposed adaptive tests for detecting changes in the diffusion and drift parameters in ergodic diffusion process models. When any change in the diffusion or drift parameter is detected by this or any other method, the next question to consider is where the change point is located. Therefore, we propose the method to estimate the change point of the parameter for two cases: the case where there is a change in the diffusion parameter, and the case where there is no change in the diffusion parameter but a change in the drift parameter. Furthermore, we present rates of convergence and distributional results of the change point estimators. Some examples and simulation results are also given. K E Y W O R D S adaptive tests, change point estimation, diffusion processes, discrete observations, test for parameter change 1

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Section: Resultsmentioning

confidence: 99%