Ways to estimate volatility
       Historical Volatility (HV)
       Parkinson's Historical Volatility (HL_ HV)
       Implied Volatility (IV)
       Implied Volatility Index (IV Index)
       Some Advanced Methods for Volatility estimation
              Simple Moving Average (SMA)
              Exponentially Weighted Moving Average (EWMA)
              Logarithmic Garman Klass (LGK)

IVolatility Education

Ways to estimate volatility

Logarithmic Garman Klass (LGK)

Volatility metric for a given day

where is volatility metric ( , , ?)
Ot, Ht, Lt, Ct are respectively, open, high, low, and close price for the day t. Thus the volatility metric is a combination of the overnight, high/low and open/close range. Such a volatility metric is a more efficient measure of the degree of volatility during a given day. This metric is always positive. Thus, expected volatility can be calculated from the following recurrent formula

The advantage of the logarithmic transform is that the residuals in the forecasting equation are approximately normal.

Expected volatility is
The parameter can be taken equal to 0.9049.
Initial value is taken as:
where - is the mean of close-to-close returns: .

The following chart shows ordinary historical volatility (calculated as standard deviation of stock's returns), High Low Historical Volatility, volatility calculated by the EWMA method on the basis of the initial volatility taken as a standard deviation, and volatility calculated by using the LGK model.