Separating Information Maximum Likelihood Method for High-Frequency Financial Data. 1st ed. 2018

種類:

電子ブック

責任表示:

by Naoto Kunitomo, Seisho Sato, Daisuke Kurisu

出版情報:

Tokyo : Springer Japan : Imprint: Springer, 2018

著者名:

• Kunitomo, Naoto.
• Sato, Seisho.
• Kurisu, Daisuke.
• SpringerLink (Online service)

シリーズ名:

JSS Research Series in Statistics ;

ISBN:

9784431559306 [4431559302]    

注記:

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

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