Realised kernels can accurately measure the variation in stock prices even with market noise. By using specific weights, these estimators can provide reliable results with minimal error. They can handle different types of data, account for market frictions, and deal with noisy data over time. Through simulations and real-world examples, it has been shown that realised kernels are effective tools for analyzing stock price variations in the presence of noise.