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Main Authors: Akahori, Jirô, Namba, Ryuya, Watanabe, Atsuhito
Format: Preprint
Published: 2023
Subjects:
Online Access:https://arxiv.org/abs/2310.02160
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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