---
title: "Rolling correlation"
description: "Rolling correlation is the correlation of two return series over a moving window, so it shows how their relationship changed instead of one average."
canonical_url: "https://fincanva.com/docs/analysis/rolling-correlation"
last_updated: "2026-09-12"
md_url: "https://fincanva.com/docs/analysis/rolling-correlation.md"
---

# Rolling correlation

Rolling correlation is the correlation between two return series measured over a window that slides forward through time, producing one correlation value per date instead of a single figure for the whole period. Where a [full-period correlation](/docs/analysis/correlation-matrix) compresses years of behavior into one number, a rolling correlation keeps the timeline: it shows when two assets moved together, when they went their own ways, and — most usefully — whether the relationship held up when it mattered. A full-period correlation of 0.4 can be the average of a long calm stretch near 0.1 and a few violent months near 0.9, and the two situations mean entirely different things for a portfolio.

**Also seen as:** moving-window correlation, rolling-window correlation, time-varying correlation

## How is a rolling correlation calculated?

A rolling correlation applies the ordinary correlation calculation repeatedly, each time to the most recent window of observations only.

$$
\rho_t = \operatorname{corr}\big(r^A_{t-w+1 \dots t},\; r^B_{t-w+1 \dots t}\big)
$$

where: $r^A$ and $r^B$ are the two series' per-period returns, $w$ is the window length in periods, and $\rho_t$ is the correlation of the two series across the window ending at date $t$. Each step forward drops the oldest observation and adds the newest, so the window "rolls" and the output is a series in the range −1 to +1.

Two properties follow from the shape of that calculation. The series cannot begin until the window is full, so the first $w$ periods of history produce no value at all. And the window length is a trade-off: a short window reacts quickly to a change in the relationship but jumps around on noise, while a long window is smooth but slow, still reporting a crisis-era correlation months after the crisis ended. Windows from a few months to a few years are standard practice.

**Fincanva's window is one year.** Wherever Fincanva computes a rolling correlation it rolls a one-year window, so every value you read is the correlation of the twelve months ending on that date — not the whole history, and not the last quarter. The window is part of the reading, not a technical footnote: a correlation of 0.8 on a one-year window and a correlation of 0.8 on a one-month window are different statements about the same pair. It also fixes where the series can start: the first year of any pair's shared history produces no value, because until then there is no full window to compute.

## Why does correlation spike in a crash?

Correlation between risk assets rises sharply in a sell-off because in that regime a single force — investors reducing exposure across the board — dominates the individual reasons the assets would otherwise move apart. Positions are sold because they can be sold, not because of what they are, so shares, credit, and often assets held precisely as diversifiers all fall in the same weeks.

The practical consequence is uncomfortable: diversification measured on calm data overstates the protection it provides in exactly the periods it is meant to protect against. A pair of holdings that spent a decade at 0.2 and combined into a genuinely smoother portfolio can behave like one holding for the three months that produce most of the [max drawdown](/docs/analysis/max-drawdown). A rolling correlation is how that becomes visible; a single full-period figure hides it by construction.

## Worked example: two sleeves that decouple, then do not

Take two sleeves of a strategy — a broad equity sleeve and a corporate-credit sleeve — and compute their correlation on a rolling window across a ten-year backtest.

| Period | Rolling correlation | What it means |
|---|---|---|
| Calm years (most of the decade) | 0.25 to 0.40 | the sleeves move partly independently; combining them lowers portfolio [volatility](/docs/analysis/volatility) |
| The three crash months | 0.85 | both sleeves fall together; the pair behaves close to a single asset |
| Full-period figure | 0.42 | a number that describes neither regime |

The full-period 0.42 is not wrong — it is the correct average — but it is the answer to a question nobody asked. The diversification benefit the 0.25–0.40 stretch implies is real for nine years and largely absent for the quarter that produced the worst of the drawdown, and only the rolling series shows that the two facts are about the same pair of sleeves.

{/* VISUAL: chart — one rolling-correlation series across ten years, y-axis -1 to +1, crash quarter shaded, full-period average as a flat dashed line; axis labelled "rolling window", never a window length — tracked in VISUAL_BACKLOG */}

## What counts as a good value?

There is no good value, only a reading. A rolling correlation near +1 means the two series have been moving almost identically over that window, near 0 means recent moves have been largely unrelated, and near −1 means they have been moving in opposite directions. What a rolling series adds over a single figure is *stability*: a pair that holds a steady 0.3 through several market regimes has behaved differently from a pair averaging 0.3 by alternating between 0.0 and 0.9, even though both report the same full-period number. Correlation also says nothing about magnitude — two series can be perfectly correlated while one moves five times as far, which is what [beta](/docs/analysis/beta) measures instead. Fincanva does not tell you which correlations to seek or avoid in a portfolio — see [Is this financial advice?](/docs/investing-theory/is-this-financial-advice).

## Does Fincanva show a rolling correlation?

Fincanva does not currently display a rolling-correlation chart on any screen in the app. Correlations themselves are shown: a Combined's analysis has a correlations page carrying a [correlation matrix](/docs/analysis/correlation-matrix) and a comparison against [factors](/docs/analysis/factor-roster). What those tables report is a single figure per pair, scoped to the whole run or to one calendar year — not a value per date, which is what makes a series rolling. The term is documented here because it is standard vocabulary for reading a diversified strategy, and because the difference between one figure and a series is exactly what the sections above are about.

*Backtests show what would have happened — not what will. Fincanva provides no financial advice — see [Is this financial advice?](/docs/investing-theory/is-this-financial-advice).*