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RiskUpdated daily after close · as of 2026-09-04

Realized Correlation: How Much of the Index's Calm Is Cancellation

Index volatility is average single-stock volatility scaled by correlation. So we measure all three the same way — the index, the average sector, the average large stock, over the same 21 sessions — and solve for the correlation that separates them. When it is near zero, violent single-name moves point in different directions and cancel before they reach the surface: the headline reads calm because the market is a hundred separate trades, not one. This is the realized twin of Volatility Premium, which covers what the options market expects correlation to be.

Today's reading

Over the 21 sessions ending 2026-09-04, the average pairwise correlation across the 100 largest stocks was 0.05 — the 1st percentile of all 3,942 days since 2011. SPY realized 8.0% annualized volatility while the average member realized 30.5%, a ratio of 3.81× (98th percentile). The nine sectors are at 0.05 (1st percentile since 1999). State: Moving independently — and the index is quieter than its own median at the same time, the configuration section 04 tests.

Sources, methodology & freshnessLast updated 2026-09-04 · Open ↓
Source
Our own ~5,500-symbol daily price database (TradeStation) plus the delisted-symbol graveyard, with SPY as the index reference and the nine original sector SPDRs (XLK, XLF, XLE, XLV, XLI, XLY, XLP, XLU, XLB) as the sector basket. Market-cap ranking from the Polygon share-class snapshot, carried by each name’s split-adjusted price change.
Methodology
21-session annualized realized volatility from daily log returns for the index, each sector and each basket member; average pairwise correlation solved from N²·σ²(basket) = Σσᵢ² + ΣΣρᵢⱼσᵢσⱼ with the basket’s volatility measured directly; membership re-picked monthly from trailing data only (minimum $5 price, $5M median daily dollar volume, 60 names priced through the window); regime states at fixed percentiles (20/50/80/95) of each series’ own history; forward SPY returns sampled at month-ends.
Updates
Recomputed after every US trading close as part of the daily pipeline.Last: 2026-09-04
Maintained & reviewed by Yuriy Matso — methodology shown on the page.
Realized correlation2026-09-04
0.05
Moving independently1st percentile since 2011
Index vol
8.0%
Avg stock
30.5%
Ratio
3.81×

Volatility-weighted average pairwise correlation across the 100 largest common stocks priced through the full 21-session window, solved from the basket's own realized volatility. Regime bands sit at fixed percentiles (20/50/80/95) of the series' own history: 0.15 / 0.27 / 0.42 / 0.59.

01

Three volatilities, one window

The index, the average sector and the average large stock, each as a z-score against its own 2011+ history so three very different levels share one axis. In a crash all three rise together — that is correlation going to one, and it is why 2020 and 2022 show as a single fused spike. The configuration worth noticing is the opposite one: the blue line falling away from the orange, meaning the stocks got louder and the index got quieter at the same time.

Sep 7, 2021Sep 4, 2026
-0.7σ0.9σ2.4σ20222023202420252026-0.3σ0.2σ1.6σ
Index (SPY)Average sectorAverage single stockSPY
21-session annualized realized volatility, expressed as a z-score against each series' own history (2011-01-03 → 2026-09-04) and drawn as a 42-session trailing average so three noisy dailies can be compared at a glance. Index = SPY; sectors = the nine original SPDRs, equal-weighted; single stock = the 100 largest common stocks, point-in-time membership, with SPY behind on its own scale. The cards below carry today's unsmoothed readings.
Index (SPY)
8.0%
-0.70σ vs its own history
Average sector
15.8%
-0.15σ vs its own history
Average single stock
30.5%
+0.54σ vs its own history
02

How far apart they are

The same three lines as a distance instead of three levels: how many times the index's own volatility the average stock and the average sector are running. A ratio rather than a gap in percentage points, because a gap widens whenever the whole market gets louder — in March 2020 every spread was enormous and none of it meant stocks had stopped moving together. The ratio strips the level out and leaves only what survives aggregation. At 1.0 the index is as volatile as its parts, which is what perfect correlation looks like.

Sep 7, 2021Sep 4, 2026
02.55202220232024202520263.41.7
Average single stock ÷ indexAverage sector ÷ indexSPY
Average single-stock and average sector 21-session realized volatility as a multiple of SPY's, drawn as a 10-session trailing average (2011-01-03 → 2026-09-04). The dashed guide at 1.0 marks an index exactly as volatile as its own components. At publication (2026-09-04), unsmoothed: single stocks 3.81× (98th percentile), sectors 1.97× (99th percentile).
Average single stock ÷ index
3.81×
98th percentile since 2011
Average sector ÷ index
1.97×
99th percentile since 2011

This is nearly the same information as the correlation below, in a unit that needs no algebra: for a large equal-weighted basket ρ̄ ≈ 1/ratio². The two are not identical here, and the reason is worth knowing — this ratio divides by the CAP-WEIGHTED index, while ρ̄ is solved from the equal-weighted basket's own volatility. When a handful of names carry most of the index the two denominators come apart, so 1/ratio² has sat a median 0.04 away from the solved correlation across this history. We publish the ratio because it is the one you can check against a screen of quotes, and the correlation because it is the one that compares to what the options market prices. Neither gets its own forward-return study: that would be the section after next in different units.

03

The correlation that separates them

One number instead of three lines. For an equal-weighted basket, N²·σ²(basket) = Σσᵢ² + ΣΣρᵢⱼσᵢσⱼ — and because the basket's own volatility is measured directly from its daily returns, everything in that identity is known except the average correlation. Solving for it gives the same quantity Cboe's implied-correlation index estimates from option prices, except computed from what actually happened.

Sep 7, 2021Sep 4, 2026
00.51202220232024202520260.10.0
Largest 100 stocksNine sectorsSPY
Volatility-weighted average pairwise correlation over trailing 21 sessions: the 100 largest common stocks (2011-01-03+) and the nine original sector SPDRs (1999-06-02+). Lines are 10-session trailing averages; dashed guides mark the stock series' own 20th/50th/80th percentiles, measured on the unsmoothed dailies. At publication (2026-09-04), unsmoothed: stocks 0.05, sectors 0.05.

The two baskets answer different questions. Sector correlation asks whether the market's big blocks are moving as one; stock correlation asks the same of individual companies, and sits higher because names inside a sector share its factor. They can and do diverge — a market can rotate violently between sectors while the stocks inside each one move together.

04

What happened next — and the honest answer is very little

The test we run before shipping anything. Sorted into quartiles of its own history, single-stock correlation has carried no dependable directional information about SPY. The highest quartile edges the baseline at six months and the lowest quartile trails it, but the gaps are small, the buckets hold about fifty month-ends each, and the tilt has an unremarkable explanation: correlation spikes in selloffs, and selloffs mean-revert. Read this table as the reason not to trade the gauge.

Correlation regimeMonth-endsNext 21 sessionsNext 63 sessionsNext 126 sessions
Lowest quartile — stocks moving independentlyToday53+0.69% · 62% win+2.36% · 78% win+5.90% · 79% win
Second quartile46+1.78% · 80% win+3.70% · 80% win+6.28% · 80% win
Third quartile42+0.69% · 60% win+2.62% · 74% win+5.19% · 78% win
Highest quartile — moving as one47+1.36% · 66% win+4.10% · 75% win+7.55% · 87% win
All month-ends188+1.13% · 67% win+3.19% · 77% win+6.26% · 81% win

Average SPY price return and share of positive outcomes from each month-end, 2011+. Observations are sampled at month-ends so the 21-session horizon does not overlap; the 63- and 126-session horizons still do, so effective sample sizes there are smaller than the counts suggest. Dividends excluded.

The configuration this page exists to name — low correlation and an index quieter than its own median — is split out below against each half on its own. It is not distinguishable from the baseline either. Calm manufactured by cancellation has not, in this record, been calm that ended badly on any schedule you could trade.

Correlation regimeMonth-endsNext 21 sessionsNext 63 sessionsNext 126 sessions
Low correlation AND quiet indexToday42+0.88% · 63% win+2.59% · 83% win+5.60% · 80% win
Low correlation, index NOT quiet11-0.02% · 55% win+1.27% · 56% win+7.21% · 78% win
Quiet index, correlation not low52+0.63% · 65% win+1.71% · 73% win+4.94% · 81% win
All month-ends188+1.13% · 67% win+3.19% · 77% win+6.26% · 81% win

Same construction. "Quiet index" means SPY's 21-session realized volatility sat at or below its own 2011+ median on that month-end.

05

The longer record — sectors since 1999

Per-name price history starts in 2011 for us, but the sector SPDRs reach back to 1999 — through the dot-com top, the GFC and 2020. On that longer record the same test does produce a gap, and it is the one result on this page worth arguing about: the lowest-correlation month-ends were followed by an essentially flat SPY six months later, against a clearly positive baseline.

Correlation regimeMonth-endsNext 21 sessionsNext 63 sessionsNext 126 sessions
Lowest quartile — sectors moving independentlyToday79-0.16% · 51% win+0.09% · 62% win+0.03% · 59% win
Second quartile87+1.18% · 67% win+1.62% · 67% win+3.31% · 71% win
Third quartile75+0.85% · 67% win+2.43% · 71% win+4.71% · 80% win
Highest quartile — sectors moving as one83+1.08% · 66% win+4.17% · 75% win+7.50% · 77% win
All month-ends324+0.76% · 63% win+2.10% · 69% win+3.98% · 72% win

Average SPY price return and share of positive outcomes from each month-end, 1999+, using the nine-SPDR correlation series. Same overlap caveat at 63 and 126 sessions.

Why we do not lead with it. Those month-ends are not 79 independent draws. They cluster hard — 10 in 2000, 8 in 2017, 8 in 2026, 7 in 2025, and 16 other years carrying the rest — so the result rests on a handful of episodes, and the largest of the pre-2020 clusters sits in 2000, immediately before the worst bear market in the sample. That is a genuine historical rhyme and a terrible sample size at the same time. It is also why the current sector reading is worth watching rather than acting on: correlation has been drifting structurally lower since 2016, before XLC ever existed, so today's low percentile is partly a regime that has not reverted rather than a cyclical extreme.
Jun 2, 1999Sep 4, 2026
00.512000200520102015202020250.0
SPY
Volatility-weighted average pairwise correlation across the nine original sector SPDRs, trailing 21 sessions and drawn as a 10-session trailing average (1999-06-02 → 2026-09-04), with SPY behind. XLRE (2015) and XLC (2018) are excluded on purpose: adding members mid-series would move the measure on a composition change and read as a regime shift.
06

The extremes

One entry per month so a single quiet stretch cannot fill the table. The right-hand column is the whole argument: at the top of the record, the index and the average stock are almost the same number — there is nowhere to hide. At the bottom, the average stock runs several times the index and the index says nothing about it.

Lowest correlation on record
Dateρ̄Index volAvg stock
2023-12-150.027.2%28.5%
2017-11-080.034.5%18.4%
2024-07-080.045.7%21.5%
2025-10-060.045.4%26.4%
2026-09-020.047.0%30.4%
2017-08-070.043.6%15.3%
Highest correlation on record
Dateρ̄Index volAvg stock
2020-03-160.8674.7%82.5%
2011-08-300.8147.4%56.4%
2011-09-010.7946.7%53.2%
2020-04-060.7691.7%105.6%
2015-09-150.7330.8%34.2%
2011-12-080.7227.7%34.7%
07

What this gauge cannot tell you

Four limits, stated plainly. It is backward-looking by construction: a 21-session window tells you the correlation that just happened, and correlation is the fastest-moving variable in the market — it goes to one in a selloff, which is exactly when a low reading stops being true. For what the market expects, read the implied side on Volatility Premium. It is direction-blind: the tables above are the only directional claims here, and the single-stock one is a null. The basket is the largest 100 only: it is re-picked monthly from trailing data and includes delisted names, so it is point-in-time rather than today's winners applied backward — but small caps have their own correlation regime and are not in it. Levels are not comparable across eras: index composition has concentrated dramatically since 2011, and a cap-weighted index made of fewer effective bets behaves differently from one made of many. Percentiles against the same construction are the honest read. The same phenomenon in return space is Return Dispersion; in factor space it is Momentum Churn.

How Realized Correlation Works

  1. 1
    Measure three volatilities the same way
    Twenty-one-session realized volatility, annualized, for SPY itself, for each of the nine original sector SPDRs, and for each of the 100 largest U.S.-listed common stocks. One window, one formula, three levels of aggregation — so the differences between the lines are about correlation and nothing else.
  2. 2
    Pick the basket point-in-time, delisted names included
    The 100 stocks are re-picked on the first session of every month from the largest common stocks by estimated market cap, drawn from the live universe AND our delisted graveyard. A company that was in the top 100 in 2014 and no longer exists is present in every window it actually traded through, which is what keeps the history from flattering itself.
  3. 3
    Back out the average pairwise correlation
    For an equal-weighted basket, N²·σ²(basket) = Σσᵢ² + ΣΣρᵢⱼσᵢσⱼ. The basket’s own volatility is measured directly from its daily returns, so the equation solves for ρ̄ — the volatility-weighted average correlation between the members. It is Cboe’s implied-correlation math run on realized prices instead of option quotes.
  4. 4
    Read it against its own record, then against the outcomes
    The reading is expressed as a percentile of its full history and a regime state at fixed percentiles (20/50/80/95). The forward-return tables are sampled at month-ends so the 21-session horizon does not overlap itself, and they are published whatever they say — which at the single-stock level is: nothing reliable.

Who Uses Realized Correlation

Risk managers
Index-level volatility is average single-stock volatility scaled by correlation. When ρ̄ is near zero, a calm VIX is being manufactured by cancellation, and any shock that re-correlates the market forces index volatility up violently from a suppressed base.
Stock pickers
Low correlation is the environment selection gets paid in — and the environment where being wrong costs the most. The single-stock volatility line is the size of that bet, in annualized percent.
Options traders
The realized scoreboard for the dispersion trade. Compare it against what the options market is charging on Volatility Premium (VIXEQ−VIX and COR3M): priced correlation versus the correlation that actually happened.
Allocators
Diversification is only worth what correlation lets it be worth. The sector series reaches back to 1999, which covers two full cycles of correlation collapsing into a top and snapping back in a bust.

Pro Tips

01
The gap is the story, not any one line
All three volatilities rise together in a crash — that is correlation going to one. The configuration worth noticing is the one where they separate: the index line falling while the single-stock line holds or rises.
02
The ratio is the scale-free version
Average single-stock volatility divided by index volatility says the same thing in a unit that travels. It strips out whether the whole market happens to be volatile and leaves only how much of it survives aggregation.
03
Correlation has been drifting down for a decade
The low-correlation months cluster in 2000, 2006, 2017, and then heavily in 2024–2026. Some of that is cyclical and some is a regime that has not reverted, so a low percentile today is less exceptional than the number alone suggests. Compare recent years to recent years.
04
The sector series is the one with history
The nine-SPDR basket starts in 1999 and covers the dot-com top, the GFC and 2020. The single-stock basket starts in 2011 because that is where our per-name price history begins.

Common Issues & Solutions

Is this the same as implied correlation (COR3M)?
No, and the pairing is the point. COR3M is what option prices say correlation will be over the next three months; this measures what correlation actually was over the last 21 sessions. Read them together on Volatility Premium — priced correlation against realized correlation — and the gap between them is its own signal.
Why does ρ̄ differ from the plain average of the pairwise correlations?
Because it is volatility-weighted. The formula solves for the correlation that reproduces the basket’s actual volatility, so pairs of high-volatility names count for more than pairs of quiet ones — exactly as they do in a portfolio. That is also how Cboe defines its implied-correlation indexes, which keeps the two comparable.
Is the basket survivorship-biased?
Deliberately not. Membership is re-picked monthly from trailing data only, and the candidate pool includes the delisted names in our CSV graveyard, so companies that later failed are in the history at full weight for the windows they traded. Market cap is estimated by carrying our share-count snapshot by each name’s own split-adjusted price change, which holds share count fixed — a percent or two of error against a ranking cut at the hundredth name.
Why nine sectors and not eleven?
XLRE launched in 2015 and XLC in 2018. Adding members mid-series would move the measured correlation on a composition change and read as a regime shift, so the sector basket is the nine originals for the whole run. The downtrend in sector correlation starts in 2016–2017, before XLC existed, so it is not an artifact of that carve-out.
Does low correlation predict a selloff?
At the single-stock level our test says no — correlation quartiles produced forward returns close to the baseline in both directions. At the sector level, with 27 years of history, the lowest-correlation month-ends were followed by roughly flat SPY six months out against a clearly positive baseline. That result is real but heavily clustered in a handful of episodes, so hold it as a base rate and do not trade it.

Frequently Asked Questions

What is realized correlation?
The average pairwise correlation between stocks that actually occurred over a recent window — here, 21 trading sessions — measured from prices rather than inferred from option prices. It answers whether the market is currently one trade or a hundred separate ones.
How can the VIX be low while individual stocks are volatile?
Index volatility is roughly average single-stock volatility multiplied by the square root of correlation. When correlation is near zero, violent single-name moves point in different directions and cancel inside the index, so the headline volatility reads calm while the average stock swings several times as hard. This page measures both halves and the correlation between them.
How is the average correlation calculated?
From the identity linking an equal-weighted basket’s volatility to its members’: N²·σ²(basket) = Σσᵢ² + ΣΣρᵢⱼσᵢσⱼ. Every term except ρ̄ is measured directly from 21 sessions of daily returns, so the equation solves for the volatility-weighted average correlation — the same construction Cboe uses for its implied-correlation indexes.
Is low correlation bullish or bearish?
Neither reliably. Across our 2011+ single-stock history, forward SPY returns after low-correlation month-ends were close to the all-month baseline. The longer sector history since 1999 does show the lowest-correlation months followed by roughly flat six-month returns against a positive baseline, but those months cluster in 2000, 2006, 2017 and 2024–2026 — a handful of episodes rather than a large independent sample.
What does low correlation mean for hedging?
That index hedges are cheap for a reason that can vanish overnight. Diversification is doing the work volatility protection would otherwise do, and correlation is the fastest-moving variable in the market: it goes to one in a selloff, which is precisely when an index-hedged book discovers its single-name risk was never hedged.
Where does the data come from?
Our own daily price database — roughly 5,500 symbols from TradeStation, plus the delisted names kept in our CSV graveyard — with SPY as the index reference and the nine original sector SPDRs as the sector basket. No options data is involved; this is entirely a realized measure.

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Last updated: 2026-09-04