Bootstrapping Realized Volatility in the presence of Intra-day Periodicity

When looking at prices in the stock market, it becomes clear that these prices are rather volatile. This means that prices vary over the day. Estimating the degree of variation through the day is of great importance to practitioners in the financial markets, and has therefore become a popular topic in financial econometric literature. The more prices are observed throughout the day the more accurately one can estimate the daily volatility. However, as is the problem with any estimation, we cannot say with 100% certainty that the estimated value is equal to the true value. What we can do is construct a confidence interval around our estimate. A confidence interval states that the true value lies within a specified range with a certain probability. In order to do this we need two things: we need to know the variance of our estimator and we need to know its distribution. Although the first issue can be dealt with, the second issue proves itself to be difficult. The goal of my bachelor thesis was to investigate the effect of assuming an intra-day periodicity factor in the volatility of the stock price on the ability of the bootstrap methods to provide better confidence intervals