Key Takeaways
- A herd-behavior gauge: COR1M reflects how closely options expect top S&P 500 stocks to move together.
- Built from two prices: it compares S&P 500 index option volatility with single-stock option volatility.
- Read against 0 and 100: higher readings mean less expected diversification inside the index.
- Not the VIX: the VIX sizes the expected move, COR1M says how tightly stocks move together.
- Earnings bend it: stock-specific events lift single-stock volatility and push readings down.
COR1M is the Cboe 1-Month Implied Correlation Index, a measure of how closely the options market expects the largest stocks in the S&P 500 to move together over the next month. Cboe calculates it by comparing the implied volatility of S&P 500 index options with the implied volatility of options on the index's 50 biggest stocks, and the result is quoted as a correlation times 100, with 0 meaning stocks priced to move independently and 100 meaning stocks priced to move in lockstep. Cboe's implied correlation overview describes it as "a gauge of herd behavior" and "the market's expectation of future diversification benefits."
That is the whole job of the index. It does not forecast direction, and it does not say how volatile the market will be. It says how tightly the index's largest stocks are expected to move as a group rather than on their own news.
What the COR1M Index Measures
The short version: COR1M is an average correlation backed out of option prices, not measured from past returns.
Correlation describes how two price series move relative to each other. A correlation of 1 means two stocks always move in the same direction by proportional amounts; 0 means their moves are unrelated. An index full of highly correlated stocks behaves like one large stock, while an index of weakly correlated stocks gets a diversification benefit, because one stock's drop is often offset by another's gain.
Implied correlation is the version of that number embedded in option prices. If index options are expensive relative to options on the stocks inside the index, the options market is effectively pricing the stocks to move together. If index options are cheap relative to the components, it is pricing them to move apart. We cover the general concept in our piece on implied correlation in options pricing; COR1M is the specific, published version of that number for the S&P 500.
Cboe began publishing a three-month version, COR3M, in July 2021, and in July 2022 announced eight more, completing a suite with one-, three-, six-, nine- and 12-month tenors (COR1M, COR3M, COR6M, COR9M and COR1Y) and a set of three-month delta-skew indices. The same release calls the suite "the first widely disseminated market estimates of the average correlation of the stocks that comprise the S&P 500," with values disseminated four times per minute during the trading day.
The VIX tells you how far the index is expected to move. COR1M tells you how tightly the largest stocks are expected to move together.
How COR1M Is Calculated
The core idea: index variance equals the variance of its parts plus a term for how they move together, so you can solve for the correlation.
According to the Cboe implied correlation white paper, the calculation follows Harry Markowitz's portfolio model and uses four steps:
- Pick the basket. Take the 50 largest S&P 500 stocks by float-adjusted market capitalization, set from the prior day's close.
- Reweight it. Rescale those 50 weights so they sum to 100%.
- Collect the volatilities. Use at-the-money implied volatilities, defined as the strike where the option delta equals 0.5, for the index and for each stock, at the index's tenor (one month for COR1M).
- Solve for correlation. Subtract the variance an uncorrelated basket would have from the index's implied variance, then divide by the sum of the pairwise weighted volatility products.
In plain arithmetic, the formula is: implied correlation equals (index implied variance minus the sum of each weight squared times that stock's variance) divided by (twice the sum, across every pair of stocks, of both weights times both volatilities). The white paper notes that the calculation "does not rely on any options pricing model" and uses at-the-money implied volatilities as its inputs.
A worked example with two stocks. Suppose a toy index holds just XYZ and ABC at 50% each, both stocks carry an implied volatility of 30%, and options on the index imply 24%. Work it in variance terms, where variance is volatility squared:
- Index implied variance: 0.24 × 0.24 = 0.0576.
- Uncorrelated basket variance: (0.5² × 0.30²) + (0.5² × 0.30²) = 0.0225 + 0.0225 = 0.045.
- Pairwise term: 2 × 0.5 × 0.5 × 0.30 × 0.30 = 0.045.
- Implied correlation: (0.0576 − 0.045) ÷ 0.045 = 0.0126 ÷ 0.045 = 0.28, which an index quoted like COR1M would show as 28.
The reference points of 0 and 100 fall out of the same arithmetic, though the formula itself has no hard limits. For example, if index options in this toy case implied 30%, the same as each stock, index variance would be 0.09, and (0.09 − 0.045) ÷ 0.045 gives 1.0, a reading of 100: the two stocks are priced to move as one. If index options implied about 21.2%, index variance would be roughly 0.045, and the reading would be 0: no correlation priced at all.
Now hold the index at 24% and suppose both stocks' implied volatility rises to 32% because each has a company-specific event coming up. The uncorrelated basket variance becomes 2 × 0.25 × 0.1024 = 0.0512, the pairwise term is also 0.0512, and the reading falls to (0.0576 − 0.0512) ÷ 0.0512 = 0.125, or 12.5. Nothing about how the stocks relate to each other changed in this example; single-stock volatility rose and index volatility did not, so measured correlation dropped.
How COR1M Differs From the VIX and Dispersion
The neighbor it gets confused with: the VIX family. COR1M and the VIX both come from S&P 500 index options, but they answer different questions.
- What it measures: the VIX estimates the expected size of S&P 500 moves over 30 days; COR1M estimates how tightly the 50 largest stocks are expected to move together.
- Inputs: the VIX uses a strip of S&P 500 index options only; COR1M uses at-the-money index options plus options on the 50 largest index stocks.
- Units: the VIX is quoted in annualized volatility points; COR1M is quoted as a correlation times 100.
- What moves it: the VIX rises whenever index options get more expensive; COR1M rises only when index options get more expensive relative to the component options.
The SKEW index is a third angle on the same option market: it measures how lopsided the expected distribution is, not its size or how much of it is shared. A full read of S&P 500 option pricing uses all three.
Dispersion is the mirror image of correlation. Cboe's S&P 500 Dispersion Index (DSPX) measures expected dispersion over the next 30 calendar days from index and single-stock option prices, using a modified version of the VIX methodology. Where COR1M expresses the gap between index and component volatility as a correlation, DSPX expresses it as how widely the components are expected to spread apart. Cboe's overview frames both as measures of the same thing: the diversification benefit the market expects from holding the index.
Realized correlation is the other neighbor. It is measured from past daily returns, while COR1M is priced forward from today's options. The two can diverge for long stretches, and the gap between them is what correlation and dispersion traders try to price.
Why COR1M Matters to Options Traders
What it explains: why index options and single-stock options can get cheaper or richer independently of each other.
If you sell premium on S&P 500 index options, part of what you collect is a correlation premium. Cboe's overview notes that investors pay a positive risk premium for index volatility but near-zero premiums for individual stocks, because index holders worry about "sharp increases in correlation, which evaporates diversification benefits." A low COR1M reading means index options are priced cheaply relative to the stocks inside them; whether the correlation premium itself is thin depends on how correlated the stocks turn out to be.
It also explains a pattern that confuses traders who watch market concentration. When a handful of mega-cap stocks drive the index, their stock-specific news can move the S&P 500 even while most other stocks sit still. Implied correlation captures whether options are pricing that concentrated, idiosyncratic risk or a broad, shared move.
For traders who structure positions around this relationship, Cboe's white paper describes the long dispersion trade as selling at-the-money index straddles and buying at-the-money straddles on the components, and uses implied correlation as its measure of how rich or cheap index options are. Our guide to correlation and dispersion trading walks through how those positions are built and where they go wrong. Whether any of that fits depends on your risk tolerance, account size, and experience with multi-leg positions.
A high reading is the options market pricing a crowd. A low reading is the options market pricing a collection of separate stories.
Edge Cases and Gotchas
Where the simple reading breaks down:
- Earnings season distorts it. Company-specific events lift single-stock implied volatility, as our explainer on how earnings change implied volatility shows, and that mechanically pushes COR1M down, as in the 12.5 example above. A low reading during a heavy earnings stretch may say more about the calendar than about diversification.
- It covers 50 stocks, not 500. The basket is the 50 largest index members. In the white paper's sample calculation from February 10, 2021, those 50 stocks made up 54.43% of the index's weight, so the reading says nothing direct about the smaller 450.
- Early history is back-tested. Cboe's COR1M daily history file runs from January 2006, years before the 2022 launch. Values before launch are back-tested, the approach Cboe's white paper describes for this index family, so long-run comparisons lean partly on calculated history.
- Lows have preceded spikes. In that history, COR1M closed at 6.81 on January 3, 2018 and at 82.94 on February 5, 2018, and closed at a series low of 2.93 on July 12, 2024 before reaching 39.62 on August 5, 2024. Low readings describe what is priced today; they have not ruled out a fast jump.
- Tenor matters. COR1M reacts to events inside the next month. Cboe's overview notes that the implied correlation term structure usually slopes upward in calm markets, so COR1M sitting below COR3M is normal rather than a signal in itself.
The highest close in Cboe's history file is 96.59, on October 24, 2008, during the financial crisis. That reading says the options market priced the 50 largest stocks to move almost as a single asset, which is what correlation looks like when diversification fails.
FAQ
These answers cover what traders usually ask once they have seen COR1M quoted next to the VIX and want to know what a given reading implies.
What Is a Normal COR1M Reading?
There is no fixed normal, and the range has shifted over time.
In Cboe's daily history from 2006 through 2025, which includes back-tested values from before the index launched, the median close was about 36 by our calculation, and from 2022 through 2025 it was about 21. Treat any band as historical context, not a target.
Can You Trade COR1M Directly?
COR1M is an index that Cboe publishes as a market gauge. Traders who want exposure to implied correlation usually build it from options: a dispersion trade pairs S&P 500 index options against options on the index's largest stocks.
What Is the Difference Between COR1M and COR3M?
Both use the same Cboe methodology and differ only in the implied volatility tenor. COR1M uses one-month volatilities and reacts to events inside the next month, while COR3M uses three-month volatilities and typically moves less sharply.
Why Does COR1M Often Drop During Earnings Season?
Earnings are stock-specific events, so options on reporting companies carry extra implied volatility. That raises the single-stock side of the calculation without an equal rise in index volatility, which mechanically lowers the implied correlation reading.
Where Can I Find the COR1M Index?
Cboe publishes current values and charts on its COR1M index dashboard. Cboe also distributes values through vendors such as Bloomberg and Refinitiv, and the exact symbol varies by platform.
