Assessing the Performance of Different Volatility Estimators : A Monte Carlo Analysis

We test the performance of different volatility estimators that have recently been proposed in the literature and which have been designed to deal with problems arising when ultra high-frequency data are employed: microstructure noise and price discontinuities. Our goal is to provide an extensive simulation analysis for different levels of noise and frequency of jumps to compare the performance of the proposed volatility estimators. We conclude that the MLE-F, a two-step parametric volatility estimator proposed by Cartea and Karyampas (2010), outperforms most of the well known high-frequency volatility estimators when different assumptions about the path properties of stock dynamics are used