  Jumps and market microstructure noise are stylized features of high-frequency
financial data. It is well known that they introduce bias in the estimation of
volatility (including integrated and spot volatilities) of assets, and many
methods have been proposed to deal with this problem. When the jumps are
intensive with infinite variation, the efficient estimation of spot volatility
under serially dependent noise is not available and is thus in need. For this
purpose, we propose a novel estimator of spot volatility with a hybrid use of
the pre-averaging technique and the empirical characteristic function. Under
mild assumptions, the results of consistency and asymptotic normality of our
estimator are established. Furthermore, we show that our estimator achieves an
almost efficient convergence rate with optimal variance when the jumps are
either less active or active with symmetric structure. Simulation studies
verify our theoretical conclusions. We apply our proposed estimator to
empirical analyses, such as estimating the weekly volatility curve using
second-by-second transaction price data.
