Separating Information Maximum Likelihood Method for High-Frequency Financial Data

1. Front Matter

   Pages i-viii

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2. Introduction

   • Naoto Kunitomo, Seisho Sato, Daisuke Kurisu

   Pages 1-3

3. Continuous-Time Models and Discrete Observations for Financial Data

   • Naoto Kunitomo, Seisho Sato, Daisuke Kurisu

   Pages 5-15

4. The SIML Estimation of Volatility and Covariance with Micro-market Noise

   • Naoto Kunitomo, Seisho Sato, Daisuke Kurisu

   Pages 17-28

5. An Application to Nikkei-225 Futures and Some Simulation

   • Naoto Kunitomo, Seisho Sato, Daisuke Kurisu

   Pages 29-37

6. Mathematical Derivations

   • Naoto Kunitomo, Seisho Sato, Daisuke Kurisu

   Pages 39-58

7. Extensions and Robust Estimation (1)

   • Naoto Kunitomo, Seisho Sato, Daisuke Kurisu

   Pages 59-78

8. Extensions and Robust Estimation (2)

   • Naoto Kunitomo, Seisho Sato, Daisuke Kurisu

   Pages 79-96

9. Local SIML Estimation of Brownian Functionals

   • Naoto Kunitomo, Seisho Sato, Daisuke Kurisu

   Pages 97-101

10. Estimating Quadratic Variation Under Jumps and Micro-market Noise

    • Naoto Kunitomo, Seisho Sato, Daisuke Kurisu

    Pages 103-109

11. Concluding Remarks

    • Naoto Kunitomo, Seisho Sato, Daisuke Kurisu

    Pages 111-112

12. Back Matter

    Pages 113-114

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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.

• 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)

“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)

• School of Political Science and Economics, Meiji University, Tokyo, Japan

  Naoto Kunitomo

• Graduate School of Economics, The University of Tokyo, Bunkyo-ku, Japan

  Seisho Sato

• School of Engeneering, Tokyo Institute of Technology, Tokyo, Japan

  Daisuke Kurisu

Naoto Kunitomo, Meiji University



Seisho Sato, The University of Tokyo



Daisuke Kurisu, Tokyo Institute of Technology

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