Grad Student Seminar: Yunxiao Liu
Semiparametric Inference of Integrated Volatility Functionals Using
High-frequency Financial Data
With the advent of intraday high-frequency data of financial assets since the late 1990s, the research of financial econometrics has entered into a “big data” era. New theoretical techniques using the theory of continuous time stochastic processes has been extensively developed, and new empirical evidence has been documented. In particular, due to its far-reaching applications in various fields such as risk management and option pricing, the study of volatility, which quantitatively measures the uncertainty of prices of financial assets, has drawn substantial attention from researchers. In this talk, we start with a brief introduction of concepts and technical tools commonly used in this field. Then we provide an asymptotic theory for the estimation of a general class of smooth nonlinear integrated volatility functionals, assuming the volatility process to be a sum of Ito semimartingale and a long memory part (e.g., fractional Brownian motions). These results are further extended to a setting with irregularly sampled data.