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The estimation of the volatility with high-frequency data is plagued by thepresence of microstructure noise, which leads to biased measures. Alternativeestimators have been developed and tested either on specific structures of thenoise or by the speed of convergence to their asymptotic distributions.Gatheral and Oomen (2010) proposed to use the Zero-Intelligence model of thelimit order book to test the finite-sample performance of several estimators ofthe integrated variance. Building on this approach, in this paper we introducethree main innovations: (i) we use as data-generating process theQueue-Reactive model of the limit order book (Huang et al. (2015)), which -compared to the Zero-Intelligence model - generates more realisticmicrostructure dynamics, as shown here by using an Hausman test; (ii) weconsider not only estimators of the integrated volatility but also of the spotvolatility; (iii) we show the relevance of the estimator in the prediction ofthe variance of the cost of a simulated VWAP execution. Overall we find that,for the integrated volatility, the pre-averaging estimator optimizes theestimation bias, while the unified and the alternation estimator lead tooptimal mean squared error values. Instead, in the case of the spot volatility,the Fourier estimator yields the optimal accuracy, both in terms of bias andmean squared error. The latter estimator leads also to the optimal predictionof the cost variance of a VWAP execution.

From Zero-Intelligence to Queue-Reactive: Limit Order Book modeling for high-frequency volatility estimation and optimal execution