# Calculating realized volatility of high-frequency data

I am wondering how to calculate the realized volatility. Sources such as say that the realized volatility is the sum of squared log returns sampled at a given frequency. So using 30 minute frequency the realized volatility per day would be the sum of 16 log returns observed during the day. However, I am interested in having a realized volatility estimate for each of the periods, in this case for each of the 30 minutes intervals. I do not intend to do forecasting, I am simply interested in knowing how volatile the asset is in a given period of time.

After reading this am I right to assume that, for example, a 30 minute RV would be estimated by summing up volatilities from higher frequency data of the preceeding period, so let's say the sum of 30 1-minute squared log returns?

• I changed the question to be more adequate to my needs. Hope somebody can help :) – abu Mar 20 '18 at 9:13