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...

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...

This paper analyzes several different biases that emerge from the (possibly) low-precision nonparametric ingredient in a semiparametric model. We show that both the variance part and the bias part of the nonparametric ingredient can lead to some biases in the semiparametric estimator, under...

We consider continuous-time models with a large panel of moment conditions, where the structural parameter depends on a set of characteristics, whose effects are of interest. The leading example is the linear factor model in financial economics where factor betas depend on observed...

This paper exploits the convolution structure of the kernel estimator and proposes a recursive procedure to correct the bias. The procedure is equivalent to replacing the original kernel with a sequence of kernels constructed by convoluting the original one in a specific way. In the...