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Separating Information Maximum Likelihood Method for High-Frequency Financial Data

  • Naoto Kunitomo
  • Seisho Sato
  • Daisuke Kurisu

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Also part of the JSS Research Series in Statistics book sub series (JSSRES)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Naoto Kunitomo, Seisho Sato, Daisuke Kurisu
    Pages 1-3
  3. Naoto Kunitomo, Seisho Sato, Daisuke Kurisu
    Pages 5-15
  4. Naoto Kunitomo, Seisho Sato, Daisuke Kurisu
    Pages 17-28
  5. Naoto Kunitomo, Seisho Sato, Daisuke Kurisu
    Pages 29-37
  6. Naoto Kunitomo, Seisho Sato, Daisuke Kurisu
    Pages 39-58
  7. Naoto Kunitomo, Seisho Sato, Daisuke Kurisu
    Pages 59-78
  8. Naoto Kunitomo, Seisho Sato, Daisuke Kurisu
    Pages 79-96
  9. Naoto Kunitomo, Seisho Sato, Daisuke Kurisu
    Pages 97-101
  10. Naoto Kunitomo, Seisho Sato, Daisuke Kurisu
    Pages 103-109
  11. Naoto Kunitomo, Seisho Sato, Daisuke Kurisu
    Pages 111-112
  12. Back Matter
    Pages 113-114

About this book

Introduction

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.

Keywords

Hedging and Risk Managements High-Frequency Financial Data Integrated Volatility and Covariance with Micro-Market Noise Micro-Market Adjustments Round-off Errors Separating Information Maximum Likelihood (SIML)

Authors and affiliations

  • Naoto Kunitomo
    • 1
  • Seisho Sato
    • 2
  • Daisuke Kurisu
    • 3
  1. 1.School of Political Science and EconomicsMeiji UniversityTokyoJapan
  2. 2.Graduate School of EconomicsThe University of TokyoBunkyo-kuJapan
  3. 3.School of EngeneeringTokyo Institute of TechnologyTokyoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-4-431-55930-6
  • Copyright Information The Author(s) 2018
  • Publisher Name Springer, Tokyo
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-4-431-55928-3
  • Online ISBN 978-4-431-55930-6
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • Buy this book on publisher's site
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