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