In this paper, we propose a general class of noise-robust estimators based on the existing estimators in the non-noisy high-frequency data literature. The market microstructure noise is a known parametric function of the limit order book. The noise-robust estimators are constructed as a plug-in version of their counterparts, where we replace the efficient price, which is non-observable in our framework , by an estimator based on the raw price and the limit order book data. We show that the technology can be directly applied to estimate volatility, high-frequency covariance, functionals of volatility and volatility of volatility in a general nonparametric framework where, depending on the problem at hand , price possibly includes infinite jump activity and sampling times encompass asynchronicity and endogeneity.
We develop a general class of noise-robust estimators based on the existing estimators in the non-noisy high-frequency data literature. The microstructure noise is a parametric function of the limit order book. The noise-robust estimators are constructed as plug-in versions of their counterparts, where we replace the efficient price, which is non-observable , by an estimator based on the raw price and limit order book data. We show that the technology can be applied to five leading examples where, depending on the problem , price possibly includes infinite jump activity and sampling times encompass asynchronicity and endogeneity.
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