Overview
- 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
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
Part of the book sub series: JSS Research Series in Statistics (JSSRES)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (10 chapters)
-
Front Matter
-
Back Matter
Keywords
About this book
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.
Reviews
Authors and Affiliations
About the authors
Seisho Sato, The University of Tokyo
Daisuke Kurisu, Tokyo Institute of Technology
Bibliographic Information
Book Title: Separating Information Maximum Likelihood Method for High-Frequency Financial Data
Authors: Naoto Kunitomo, Seisho Sato, Daisuke Kurisu
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-4-431-55930-6
Publisher: Springer Tokyo
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s) 2018
Softcover ISBN: 978-4-431-55928-3Published: 02 July 2018
eBook ISBN: 978-4-431-55930-6Published: 14 June 2018
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: VIII, 114
Number of Illustrations: 8 b/w illustrations
Topics: Statistical Theory and Methods, Statistics for Business, Management, Economics, Finance, Insurance, Statistics and Computing/Statistics Programs, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences