Separating Information Maximum Likelihood Method for High-Frequency Financial Data

Beschreibung

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.

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

Autor*in

Naoto Kunitomo

Themen in »Separating Information Maximum Likelihood Method for High-Frequency Financial Data«

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)

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“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)

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