In recent years, there has been an explosion of research on the volatility of stock returns. As high frequency stock price data became more readily available, there have been many proposed estimators of integrated variance which attempt to take advantage of the informational gains of high-frequency data while minimizing any potential biases that arise from sampling at such a fine scale. These estimators rely on various assumptions about the price process which can make them difficult to compare theoretically. Relying on the methods of Patton (2011a), this paper analyzes the performance of five different classes of integrated variance estimators when applied to various stocks of differing market capitalization in an attempt to discover the circumstances under which one estimator should be chosen over another.