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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/2310.02160 |
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| _version_ | 1866918089460809728 |
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| author | Akahori, Jirô Namba, Ryuya Watanabe, Atsuhito |
| author_facet | Akahori, Jirô Namba, Ryuya Watanabe, Atsuhito |
| contents | The SIML (abbreviation of Separating Information Maximal Likelihood) method, has been introduced by N. Kunitomo and S. Sato and their collaborators to estimate the integrated volatility of high-frequency data that is assumed to be an Itô process but with so-called microstructure noise. The SIML estimator turned out to share many properties with the estimator introduced by P. Malliavin and M.E. Mancino. The present paper establishes the consistency and the asymptotic normality under a general sampling scheme but without microstructure noise. Specifically, a fast convergence shown for Malliavin--Mancino estimator by E. Clement and A. Gloter is also established for the SIML estimator. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_02160 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The SIML method without microstructure noise Akahori, Jirô Namba, Ryuya Watanabe, Atsuhito Statistics Theory Probability 62G20, 60F05, 60H05 The SIML (abbreviation of Separating Information Maximal Likelihood) method, has been introduced by N. Kunitomo and S. Sato and their collaborators to estimate the integrated volatility of high-frequency data that is assumed to be an Itô process but with so-called microstructure noise. The SIML estimator turned out to share many properties with the estimator introduced by P. Malliavin and M.E. Mancino. The present paper establishes the consistency and the asymptotic normality under a general sampling scheme but without microstructure noise. Specifically, a fast convergence shown for Malliavin--Mancino estimator by E. Clement and A. Gloter is also established for the SIML estimator. |
| title | The SIML method without microstructure noise |
| topic | Statistics Theory Probability 62G20, 60F05, 60H05 |
| url | https://arxiv.org/abs/2310.02160 |