We introduce recent issues and research around volatility estimation based on high-frequency financial data. Previous studies often ignored the presence of micro-market noises, thereby obtaining misleading estimation results. In this book, we propose the separating information maximum likelihood (SIML) method.
- Ait-Sahalia, Y., and J. Jacod. 2014. High-frequency financial econometrics. University Press.Google Scholar
- Camponovo, L., Y. Matsushita, and T. Otsu. 2017. Empirical likelihood for high frequency data. Unpublished Manuscript.Google Scholar
- Christensen, Kinnebrock, and Podolskij. 2009. Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data. Unpublished Manuscript.Google Scholar
- Kunitomo, N. and S. Sato. 2008a. Separating information maximum likelihood estimation of realized volatility and covariance with micro-market noise. Discussion Paper CIRJE-F-581, Graduate School of Economics, University of Tokyo. http://www.e.u-tokyo.ac.jp/cirje/research/dp/2008.
- Kunitomo, N. and S. Sato. 2008b. Realized Volatility, Covariance and Hedging Coefficient of Nikkei-225 Futures with Micro-Market Noise. Discussion Paper CIRJE-F-601, Graduate School of Economics, University of Tokyo.Google Scholar
- Kunitomo, N. and S. Sato. 2010. Robustness of the separating information maximum likelihood estimation of realized volatility with micro-market noise. CIRJE Discussion Paper F-733, University of Tokyo.Google Scholar
- Zhou, B. 1998. F-consistency, De-volatilization and normalization of high frequency financial data. In Nonlinear modeling of high frequency time series, ed. C. Dunis, and B. Zhou, 109–123. New York: Wiley.Google Scholar