Let me first give you an overview of Advanced Historical Volatility Estimators.
Advanced historical volatility estimators can use open, high and
low prices in addition to closing price.
These are more efficient than conventional methods. It is said.
<Excerpts from the literature>
CLOSE-TO-CLOSE (C):
The simplest and most common type of calculation that benefits from only using reliable prices from closing auctions.
PARKINSON (HL):
The first advanced volatility estimator was created by Parkinson in 1980, and instead of using closing prices it uses the high and low price.
While other measures are more efficient based on simulated data, some
studies have shown this to be the best measure for actual empirical data.
GARMAN-KLASS (OHLC):
Later in 1980 the Garman-Klass volatility estimator was created. It is an extension of Parkinson which includes opening and closing prices. As overnight jumps are ignored, the measure underestimates volatility. Yang-Zhang modified the Garman-Klass volatility measure in order to enable it to handle jumps.
ROGERS-SATCHELL (OHLC):
The Rogers-Satchell volatility created in the early 1990s is able to properly measure the volatility for securities with non-zero mean. It does not, however, handle jumps (hence it underestimates the volatility).
EXPONENTIALLY WEIGHTED (C):
Exponentially weighted volatilities are rarely used, partly due to the fact they do not handle regular volatility driving events such as earnings very well. Previous earnings jumps will have the least weight just before an earnings date, and the most weight just after earnings. It could, however, be of some use for indices.
YANG-ZHANG (OHLC):
In 2000 Yang-Zhang created the most powerful volatility measure that handles both opening jumps and drift. It is the sum of the overnight volatility (close to open volatility) and a weighted average of the Rogers-Satchell volatility and the open to close volatility.
What is Drift?:
Average amount stocks are assumed to rise. The drift phenomenon refers to the tendency of stock. Prices and returns to continue drifting in the direction of an earnings surprise for many months subsequent to the public announcement of earnings.
I will be uploading two more indicators in addition to the six listed above.
-Adjusted Mean Absolute Deviation Estimator
-Adjusted Median Absolute Deviation Estimator
Finally,
I would like to thank you Balipour for publishing an excellent explanation and code.
Yoake
Reference document:
Balipour -Historical-Volatility-Estimators script
https://www.tradingview.com/script/nT8O ... ators-pig/
Ways to estimate volatility Historical Volatility
https://www.ivolatility.com/help/2.html
Price and Return's distribution
https://www.ivolatility.com/help/19.html
[PDF] MEASURING HISTORICAL VOLATILITY 2023/01/17
All 8 indicators are included. ( 9 type of Historical Volatility Estimators )
I forgot to include the "Garman Klass YZ extension" and have corrected it.
(2023/01/18) Corrected some file names.
