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Separating Information Maximum Likelihood Method for High-Frequency Financial Data

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Separating Information Maximum Likelihood Method for High-Frequency Financial Data/ by Naoto Kunitomo, Seisho Sato, Daisuke Kurisu.
Author:	Kunitomo, Naoto.
other author:	Sato, Seisho.
Description:	VIII, 114 p. 8 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Statistics . -
Online resource:	https://doi.org/10.1007/978-4-431-55930-6
ISBN:	9784431559306

Separating Information Maximum Likelihood Method for High-Frequency Financial Data
Kunitomo, Naoto.

Separating Information Maximum Likelihood Method for High-Frequency Financial Data[electronic resource] /by Naoto Kunitomo, Seisho Sato, Daisuke Kurisu. - 1st ed. 2018. - VIII, 114 p. 8 illus.online resource. - JSS Research Series in Statistics,2364-0057. - JSS Research Series in Statistics,.

1. Introduction -- 2. High-Frequency Financial Data and Statistical Problems -- 3. The SIML method -- 4. Asymptotic Properties -- 5. Simulation and Finite Sample Properties -- 6. Asymptotic Robustness -- 7. Two Dimension Applications -- 8. Concluding Remarks -- 9. References.

This book presents a systematic explanation of the SIML (Separating Information Maximum Likelihood) method, a new approach to financial econometrics. Considerable interest has been given to the estimation problem of integrated volatility and covariance by using high-frequency financial data. Although several new statistical estimation procedures have been proposed, each method has some desirable properties along with some shortcomings that call for improvement. For estimating integrated volatility, covariance, and the related statistics by using high-frequency financial data, the SIML method has been developed by Kunitomo and Sato to deal with possible micro-market noises. The authors show that the SIML estimator has reasonable finite sample properties as well as asymptotic properties in the standard cases. It is also shown that the SIML estimator has robust properties in the sense that it is consistent and asymptotically normal in the stable convergence sense when there are micro-market noises, micro-market (non-linear) adjustments, and round-off errors with the underlying (continuous time) stochastic process. Simulation results are reported in a systematic way as are some applications of the SIML method to the Nikkei-225 index, derived from the major stock index in Japan and the Japanese financial sector.

ISBN: 9784431559306

Standard No.: 10.1007/978-4-431-55930-6doiSubjects--Topical Terms:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Separating Information Maximum Likelihood Method for High-Frequency Financial Data
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520 $a This book presents a systematic explanation of the SIML (Separating Information Maximum Likelihood) method, a new approach to financial econometrics. Considerable interest has been given to the estimation problem of integrated volatility and covariance by using high-frequency financial data. Although several new statistical estimation procedures have been proposed, each method has some desirable properties along with some shortcomings that call for improvement. For estimating integrated volatility, covariance, and the related statistics by using high-frequency financial data, the SIML method has been developed by Kunitomo and Sato to deal with possible micro-market noises. The authors show that the SIML estimator has reasonable finite sample properties as well as asymptotic properties in the standard cases. It is also shown that the SIML estimator has robust properties in the sense that it is consistent and asymptotically normal in the stable convergence sense when there are micro-market noises, micro-market (non-linear) adjustments, and round-off errors with the underlying (continuous time) stochastic process. Simulation results are reported in a systematic way as are some applications of the SIML method to the Nikkei-225 index, derived from the major stock index in Japan and the Japanese financial sector.
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