I have recently compared the 30 day volatility estimate of close-close prices, Parkinson, Garman Klass (and exponentiall weighted Garman Klass), Rogers Satchell and Yang and Zhang. When testing them on the EURUSD and the S&P 500 one observes that the various estimators can vary substantially.

Because volatility is unobservable (only its realization can be measured ex post) and not constant over time one would generally prefer an estimator which uses as few datapoints as possible, especially in trading. This however causes instability in the estimate.

The whole dilemma can be described as follows: The estimated volatility should be stable if an outlier occurs that causes a large price change. On the other hand it should include the large price movement if the price movement is based on increased volatility and immediately adjust the estimated volatility to the upside. The same problem occurs for "older" data points which should have only a limited influence on the estimate overall.

Other models employ REGARCH or high frequency data for accurate estimates. But a much bigger dataset is required for such estimation procedures.

Independent of the volatility estimate the question whether market implied volatility through options prices is too high or low or can be forecastable or is a good volatility forecast is another story. So, now having an efficient estimate for volatility based on high-low prices I still have to think of how to put the number at use ;)