Overview
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.
Full Product Details
Author: Naoto Kunitomo ,
Seisho Sato ,
Daisuke Kurisu
Publisher: Springer Verlag, Japan
Imprint: Springer Verlag, Japan
Edition: 1st ed. 2018
Volume: 0
Weight: 0.454kg
ISBN: 9784431559283
ISBN 10: 4431559280
Pages: 114
Publication Date: 02 July 2018
Audience:
College/higher education
,
Professional and scholarly
,
Postgraduate, Research & Scholarly
,
Professional & Vocational
Format: Paperback
Publisher's Status: Active
Availability: Manufactured on demand
We will order this item for you from a manufactured on demand supplier.
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)
“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)
Author Information
Naoto Kunitomo, Meiji University Seisho Sato, The University of Tokyo Daisuke Kurisu, Tokyo Institute of Technology
