  We investigate the predictability of several range-based stock volatility
estimators, and compare them to the standard close-to-close estimator which is
most commonly acknowledged as the volatility. The patterns of volatility
changes are analyzed using LSTM recurrent neural networks, which are a state of
the art method of sequence learning. We implement the analysis on all current
constituents of the Dow Jones Industrial Average index, and report averaged
evaluation results. We find that changes in the values of range-based
estimators are more predictable than that of the estimator using daily closing
values only.
