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JSS Research Series in Statistics

Separating Information Maximum Likelihood Method for High-Frequency Financial Data

Authors: Kunitomo, Naoto, Sato, Seisho, Kurisu, Daisuke

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  • Gives a systematic treatment of SIML (Separating Information Maximum Likelihood) method in financial econometrics
  • Discusses a robust estimation method for integrated volatility, covariance, and hedging coefficient by using high-frequency financial data
  • Includes applications to high-frequency financial data in Japan
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eBook 44,02 €
price for Spain (gross)
  • ISBN 978-4-431-55930-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 57,19 €
price for Spain (gross)
  • ISBN 978-4-431-55928-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

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.

About the authors

Naoto Kunitomo, Meiji University

Seisho Sato, The University of Tokyo

Daisuke Kurisu, Tokyo Institute of Technology

Reviews

“The authors develop a new statistical approach, which is called the separating information maximum likelihood (SIML) method, for estimating integrated volatility and integrated covariance by using high-frequency data in the presence of possible micro-market noise. … The book is useful for students and professionals in mathematical finance.” (Pavel Stoynov, zbMath 1416.91004, 2019)


Table of contents (10 chapters)

Table of contents (10 chapters)

Buy this book

eBook 44,02 €
price for Spain (gross)
  • ISBN 978-4-431-55930-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 57,19 €
price for Spain (gross)
  • ISBN 978-4-431-55928-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Separating Information Maximum Likelihood Method for High-Frequency Financial Data
Authors
Series Title
JSS Research Series in Statistics
Copyright
2018
Publisher
Springer Japan
Copyright Holder
The Author(s)
eBook ISBN
978-4-431-55930-6
DOI
10.1007/978-4-431-55930-6
Softcover ISBN
978-4-431-55928-3
Series ISSN
2364-0057
Edition Number
1
Number of Pages
VIII, 114
Number of Illustrations
8 b/w illustrations
Topics