Yang-Zhang and other realized-volatility estimators
Volatility estimators use different parts of price history. Compare their assumptions and treatment of overnight movement before interpreting a gap against IV.
By OptionsKing. Publisher and editorial methods.
What you will learn
By the end of this lesson, you should be able to:
- Identify each estimator's inputs.
- Explain overnight and intraday treatment.
- Keep estimator choices consistent in comparisons.
Keep the dataset and window constant while changing only the estimator. The resulting range demonstrates method sensitivity, not proof that one number is the true volatility. Follow which prices each method uses, especially its handling of gaps, then choose and document a consistent comparison.
The same 30 sessions, five estimators
| Estimator | Uses | Annualized | Per day |
|---|---|---|---|
| Garman-Klass | open, high, low, close | 19.9% | 1.25% |
| Rogers-Satchell | open, high, low, close | 20.4% | 1.29% |
| Parkinson | high, low | 20.8% | 1.31% |
| Close-to-close | close only | 24.1% | 1.52% |
| Yang-Zhang | open, high, low, close, and the gap | 25.6% | 1.61% |
Now put that beside the thing you were going to use it for. The variance risk premium on this name was about 1.9 volatility points. Your choice of estimator moves the answer by 5.7. The spread between methods is three times the trailing implied-minus-close-to-close gap. This shows method sensitivity, not a known measurement error against a true value.
Pick Garman-Klass and Nike's 26 percent implied looks like a fat six-point edge. Pick Yang-Zhang and 26 percent implied sits only 0.4 points above 25.6 percent realized; that small trailing gap does not establish a trade. Same stock, same window, same option.
What each one is doing
Close-to-close takes log returns between consecutive closes. It uses two numbers out of every session and ignores everything in between. A day that opened at $74, ran to $77, broke to $72 and closed at $74.05 registers as a 0.07 percent day. That is the net close-to-close return, not a measure of the intraday path. It is a valid input for one estimator but omits information used by range methods.
Parkinson (1980) uses the high and the low of each session instead. The intuition is direct: a wide range means a volatile day whatever the close did. It is roughly five times more efficient than close-to-close on the same amount of data, meaning you need far fewer sessions for the same precision. Its blind spot is the overnight gap, which it cannot see at all.
Garman-Klass (1980), published five pages later in the same journal issue, adds the open and the close to Parkinson's high and low. More efficient again. Same blind spot on gaps, and it also assumes the stock has no drift.
Rogers-Satchell fixes the drift assumption, so it does not overstate volatility for a stock that is trending steadily. Still blind overnight.
Yang-Zhang (2000) is the one that handles the gap. Yang and Zhang built it as a combination of the overnight move, the open-to-close move and a Rogers-Satchell term, and claim in the paper that it is unbiased in the continuous limit, independent of the drift, and "consistent in dealing with opening price jumps," with "the smallest variance among all estimators with similar properties."
Why Yang-Zhang comes out highest here
Close-to-close returns include overnight movement as part of each full close-to-close change. What they omit is the intraday path and a separate gap component. Yang-Zhang explicitly separates overnight and session effects; it is not the only method in this table affected by overnight prices.
On this sample, explicitly separating overnight and intraday variation produces a higher estimate.
That gap is not noise to a premium seller. It is the single most important part of the distribution. Overnight is when earnings land, when guidance gets cut, when a competitor announces something, and it is the window in which you cannot close a position, cannot hedge and cannot use a stop. An estimator that scores 19.9 percent by ignoring the overnight session is measuring the risk you can manage and skipping the risk that assigns you.
The ordering is specific to this sample. Estimator assumptions, intraday paths, and jumps all affect the readings; another sample need not have the same ordering.
Which one to use
If you sell options and hold them overnight, Yang-Zhang. Your risk is the full 24 hours and it explicitly separates overnight and trading-session variation, while close-to-close also spans the full day. A higher estimate is not automatically more accurate or more appropriate.
If you need precision from a short window, Parkinson or Garman-Klass. Estimating volatility from ten sessions with close-to-close is nearly hopeless. The range-based estimators get you a usable number from far less data.
If you are comparing against implied, be consistent and be explicit. This is the real rule. Any of these is defensible. Switching between them, or comparing your Yang-Zhang figure against a platform's close-to-close figure, is not. Write down which one you use and never change it mid-comparison.
If your platform does not say, assume close-to-close. It is the default nearly everywhere, and on this Nike window it read 1.5 points under Yang-Zhang, which explicitly separates the gap. Close-to-close also includes its net effect; check the platform's method rather than assuming it.
The point of the whole page
Realized volatility looks like a fact and behaves like an opinion. There is no single true number for what a stock did last month, only a number produced by a method, and the methods differ by more than the edges most people are trading.
So when a screen tells you an option is trading three points above realized, the only correct first question is: realized measured how? If nobody can answer, the three points are not information yet.
Check your understanding
Does a gap make close-to-close volatility blind to overnight risk?
Show the explanation
No. The next closing price includes the net effect of the gap and that day's trading. Close-to-close does not separately identify the gap and can miss large intraday moves that reverse before the close.
Common questions
What is the Yang-Zhang volatility estimator?
A realized volatility estimator combining the overnight move, the open-to-close move and a Rogers-Satchell term. Yang and Zhang published it in 2000 and claim it is drift-independent, handles opening jumps, and has the smallest variance among estimators with those properties.
Why do volatility estimators give different answers?
Because they use different parts of each price bar. Close-to-close sees two prices a day, range estimators see the high and low but not the overnight gap, and Yang-Zhang sees all of it. On one Nike window the five span 19.9 to 25.6 percent.
Which volatility estimator should an option seller use?
Choose a method suited to your data and purpose, and keep comparisons consistent. Yang-Zhang explicitly separates overnight and intraday components; close-to-close returns also include the net overnight move. No estimator eliminates event or forecast uncertainty.
Is Parkinson volatility better than close-to-close?
More efficient, so it needs far less data for the same precision, but blind to the overnight gap. That makes it good for estimating from short windows and misleading for measuring the risk in a position held through the close.
Sources
Rules and thresholds above were checked against these documents on August 4, 2026. Exchange and broker rules change. Confirm anything you are about to act on with your own broker.
Keep reading
Do the math on your own trade
Every price in this article is an illustrative worked example, not a quote. Read Volatility for the rest of the series, and the disclaimer before you act on any of it. Selling options carries real risk of loss, and the loss can be far larger than the premium you collected.