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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2302.05885 |
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| _version_ | 1866909574327435264 |
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| author | Amorino, Chiara Jaramillo, Arturo Podolskij, Mark |
| author_facet | Amorino, Chiara Jaramillo, Arturo Podolskij, Mark |
| contents | We establish inequalities for assessing the distance between the distribution of errors of partially observed high-frequency statistics of multidimensional Lévy processes and that of a mixed Gaussian random variable. Furthermore, we provide a general result guaranteeing stable functional convergence. Our arguments rely on a suitable adaptation of the Stein's method perspective to the context of mixed Gaussian distributions, specifically tailored to the framework of high-frequency statistics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_05885 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Quantitative and stable limits of high-frequency statistics of Lévy processes: a Stein's method approach Amorino, Chiara Jaramillo, Arturo Podolskij, Mark Probability We establish inequalities for assessing the distance between the distribution of errors of partially observed high-frequency statistics of multidimensional Lévy processes and that of a mixed Gaussian random variable. Furthermore, we provide a general result guaranteeing stable functional convergence. Our arguments rely on a suitable adaptation of the Stein's method perspective to the context of mixed Gaussian distributions, specifically tailored to the framework of high-frequency statistics. |
| title | Quantitative and stable limits of high-frequency statistics of Lévy processes: a Stein's method approach |
| topic | Probability |
| url | https://arxiv.org/abs/2302.05885 |