In this paper, we use factor-augmented HAR-type models to predict the daily integrated volatility of asset returns. Our approach is based on a proposed two-step dimension reduction procedure designed to extract latent common volatility factors from a large dimensional and high-frequency returns...

In recent years, the field of financial econometrics has seen tremendous gains in the amount of data available for use in modeling and prediction. Much of this data is very high frequency, and even 'tick-based', and hence falls into the category of what might be termed big data. The availability...

We propose a new nonparametric test to identify mutually exciting jumps in high frequency data. We derive the asymptotic properties of the test statistics and show that the tests have good size and reasonable power in finite sample cases. Using our mutual excitation tests, we empirically...

This paper studies the estimation of integrated volatility functionals, which is essentially a semiparametric two-step estimation problem in the nonstationary continuous-time setting. Different from the classic i.i.d. or stationary setting, a faster-than-$n^{1/4}$ convergence rate for the...

This paper studies the estimation and inference problems for time-invariant restrictions on certain functions of the stochastic volatility process. We first develop a more efficient GMM estimator and derive the efficiency bound under such restrictions. Then we construct an integrated...