Hi all, in the attachement you can find a file , about volatility estimators. They are writen in mathematical terms, but as with any other math/statistics formula this should be explained in more simple way. Does anybody know where to find a resource that could be helpful in explaining this. I would say the 1st one (Close to close) is just plain standard deviation of returns, correct me if I am wrong? Thanks for any input
I would suggest Google. The first one, as stated in the document, is the annualised standard deviation of log returns, squared, aka variance, if memory serves.
Close-to-Close Estimator Pro [*] It has well-understood sampling properties. [*] It is easy to correct bias. [*] It is easy to convert to a form involving typical daily moves. Con [*] It is a very inefficient use of data and converges very slowly. Parkinson Estimator Pro [*] Using daily range seems sensible and provides completely separate information from using time-based sampling such as closing prices. Con [*] It is really only appropriate for measuring the volatility of a GBM process. In particular it cannot handle trends and jumps. [*] It systematically underestimates volatility. Garman-Klass Estimator Pro [*] It is up to eight times more efficient than close-to-close estimator. [*] It makes the best use of the commonly available price information. Con [*] It is even more biased than the Parkinson estimator. Rogers-Satchell Estimator Pro [*] It allows for the presence of trends. Con [*] It still cannot deal with jumps.
Personally I prefer volatility to be measured as %CV or coefficient of variation, it is relatively simple to compute and can be done with an Excel spreadsheet. The principle is to divide the standard deviation with the average. The higher the %CV, the more volatility in that stock. You can find more information here: http://www.stock-trading.me/2010/04/calculate-stock-volatility-avoid-stocks-with-highest-volatility/