Separating information maximum likelihood method for high-frequency financial data
The authors study the problem of estimating integrated volatility using financial high-frequency data. This problem became of considerable interest in the last decade with the availability of large high-frequency data including stock markets and foreign exchange markets.\newline 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.\newline The book is organised in ten chapters. Chapter 1 is introductory. In Chapter 2, the authors discuss the underlying background to volatility and high-frequency econometrics. Chapter 3 introduces the basic model and the SIML method. In Chapter 4, finite-sample properties of SIML are discussed. In Chapter 5, mathematical derivations of the asymptotic results given in Chapter 3 are presented. In Chapters 6 and 7, asymptotic robustness of SIML estimation is discussed. In Chapter 8, local SIML method is proposed as an extension of the basic SIML method. In Chapter 9, the authors propose an estimation of the quadratic version of Ito's semi-martingales including jumps. In Chapter 10, possible extensions of SIML are considered.\newline The book is useful for students and professionals in mathematical finance.
scientific article; zbMATH DE number 6905120
scientific article