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Jumps and market microstructure noise are stylized features of high-frequencyfinancial data. It is well known that they introduce bias in the estimation ofvolatility (including integrated and spot volatilities) of assets, and manymethods have been proposed to deal with this problem. When the jumps areintensive with infinite variation, the efficient estimation of spot volatilityunder serially dependent noise is not available and is thus in need. For thispurpose, we propose a novel estimator of spot volatility with a hybrid use ofthe pre-averaging technique and the empirical characteristic function. Undermild assumptions, the results of consistency and asymptotic normality of ourestimator are established. Furthermore, we show that our estimator achieves analmost efficient convergence rate with optimal variance when the jumps areeither less active or active with symmetric structure. Simulation studiesverify our theoretical conclusions. We apply our proposed estimator toempirical analyses, such as estimating the weekly volatility curve usingsecond-by-second transaction price data.

Estimating spot volatility under infinite variation jumps with dependent market microstructure noise