VOLATILITY AND JUMPS IN HIGH FREQUENCY FINANCIAL DATA: ESTIMATION AND TESTING

It has been widely accepted in financial econometrics that both the microstructure noiseand jumps are significantly involved in high frequency data. In some empirical situations,the noise structure is more complex than independent and identically distributed (i.i.d.)assumption. Therefore, it is important to carefully study the noise and jumps when usinghigh frequency financial data. In this dissertation, we develop several methods related to the volatility estimation and testing for jumps.Chapter 1 proposes a new method for volatility estimation in the case where both the noise level and noise dependence are significant. This estimator is a weighted combinationof sub-sampling realized covariances, constructed from discretely observed high frequency data. It is proved to be a consistent estimator of quadratic variation in the case with either i.i.d. or dependent noise. It is also shown to have good finite-sample properties compared with existing estimators in the literature.Chapter 2 focuses on the testing for jumps based on high frequency data. We generalizethe methods in Ait-Sahalia and Jacod (2009a) and Fan and Fan (2010). The generalized method allows more flexible choices for the construction of test statistics, and has smallerasymptotic variance under both null and alternative hypotheses. However, all these methods are not effective when the microstructure noise is significant. To reduce the influence from noise, we further design a new statistical test, which is robust with the i.i.d. microstructurenoise. This new method is compared with the old tests through Monte Carlo studies