Separating Information Maximum Likelihood Method for High-Frequency Financial Data - ISBN: 9784431559306 - (ebook) - von Naoto Kunitomo, Seisho Sato, Daisuke Kurisu, Verlag: Springer - Details - OvW eBook Shop

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Separating Information Maximum Likelihood Method for High-Frequency Financial Data


SpringerBriefs in Statistics

von: Naoto Kunitomo, Seisho Sato, Daisuke Kurisu

55,92 €

Verlag: Springer
Format: PDF
Veröffentl.: 14.06.2018
ISBN/EAN: 9784431559306
Sprache: englisch

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Beschreibungen

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.
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.
Naoto Kunitomo ProfessorGraduate School of Economics, The University of Tokyokunitomo@e.u-tokyo.ac.jpHongo 7-3-1, Bunkyoku, Tokyo 113-0033, JapanSeisho SatoAssociate ProfessorGraduate School of Economics, The University of Tokyoseisho@e.u-tokyo.ac.jpHongo 7-3-1, Bunkyoku 113-0033, Japan
Gives a systematic treatment of SIML (Separating Information Maximum Likelihood) method in financial econometricsDiscusses a robust estimation method for integrated volatility, covariance, and hedging coefficient by using high-frequency financial dataIncludes applications to high-frequency financial data in Japan

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