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Realized (historical) volatility explained

How realized volatility is calculated, how it differs from implied volatility, and why dividends and splits must be adjusted for.

Reviewed · Sources at the end · How Vyreon measures
Vyreon's per-security reports are not live yet; this page describes what they will measure. Examples are illustrative.

Realized volatility, also called historical volatility, measures how much a security's price actually moved over a past window, expressed on a yearly scale. It looks backward; implied volatility looks at what option prices imply going forward.

How Realized Volatility Is Calculated

The classic method takes the standard deviation of daily close-to-close returns and annualizes it. Other standard estimators also use each day's open, high and low (Parkinson, Garman–Klass, Rogers–Satchell, Yang–Zhang), which can capture intraday movement that closing prices miss.

Different windows answer different questions: five sessions shows the last week, while 42 sessions shows roughly the last two months.

Realized Vs Implied Volatility

Comparing the two shows how option prices relate to recent movement. When implied volatility is well above recent realized volatility, options are pricing bigger moves than the stock has been making. When it is below, the reverse.

What Realized Volatility Does Not Tell You

  • It does not predict future volatility, although volatility tends to cluster in time.
  • Dividends and splits create price jumps that are not real volatility. They must be adjusted for.

How Vyreon Measures It

Vyreon reports realized volatility over 5, 10, 21 and 42 sessions using five standard estimators, on dividend- and split-adjusted prices, so an ex-dividend day does not appear as a false move.

Related: implied volatility · relative volume

Sources

  • Michael Parkinson, "The Extreme Value Method for Estimating the Variance of the Rate of Return", Journal of Business (1980); Mark B. Garman and Michael J. Klass, "On the Estimation of Security Price Volatilities from Historical Data", Journal of Business (1980).
  • L. C. G. Rogers and S. E. Satchell, "Estimating Variance from High, Low and Closing Prices", Annals of Applied Probability (1991); Dennis Yang and Qiang Zhang, "Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices", Journal of Business (2000).
  • Benoit Mandelbrot, "The Variation of Certain Speculative Prices", Journal of Business (1963), and Robert F. Engle, "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation", Econometrica (1982): volatility clustering.
  • John C. Hull, Options, Futures, and Other Derivatives (Pearson, many editions): historical volatility from close-to-close returns.