I compared various MIDAS (mixed data sampling) regression models to predict volatility from one week to one month with different regressors based on the records of Chinese Shanghai composite index. The main regressors are in 2 types, one is the realized power (involving 5-min absolute returns), the other is the quadratic variation, computed by squared returns. And realized power performs best at all the forecast horizons. I also compare the effect of lag numbers in regression, form 1 to 200, and it doesn’t change much after 50. In 3 week and month predict horizons, the fitness result with different lag numbers has a waving type among all the regressors, that implies there exists a seasonal effect which is the same as predict horizons in the lagged variables. At last,the out-of -sample and in-sample result of RV and RAV are quite similar, but in sometimes, out-of sample performs better.