Direct answer: which forex pairs are correlated
For high liquidity pairs, correlation is most commonly observed when pairs share an underlying currency. In practice, the most likely correlated groups are:
- Currency “chains” that share a base currency (same first currency), such as EUR/USD and EUR/GBP.
- Currency “chains” that share a quote currency (same second currency), such as EUR/USD and GBP/USD.
- Pairs that are linked through a common major, such as any USD-quoted major pair versus another USD-quoted major pair.
However, “correlated” does not mean “always correlated.” Correlation varies by market conditions and by the way you measure it (for example, daily returns vs. hourly returns). So the only reliable way to say which pairs are correlated for your purpose is to compute correlation from historical returns for the time window and sampling frequency you care about.
How correlation in forex pairs works
A forex pair is two currencies quoted against each other (for example, EUR/USD is how EUR relates to USD). If two pairs share one currency, then part of their price movement is driven by the same currency’s changes versus the other side.
Common pairs in the high-liquidity set are major currency pairs that trade actively. When you compute correlation, you typically do it on a time series of returns:
- Choose a measurement: price levels or returns.
- Pick a sampling frequency: for example, changes per day or per hour.
- Pick a time window: for example, the last 3 months or last year.
- Compute correlation (often Pearson correlation) between the return series of two pairs.
Interpreting results:
- Positive correlation means both pairs tend to move in the same direction during the window.
- Negative correlation means they tend to move in opposite directions.
- Correlation near zero means little linear relationship for that window.
Even within high liquidity pairs, linear correlation can weaken if the market regime changes (for example, from steady trading to fast repricing).
Example checks for high liquidity pairs
Here are independent, verifiable checks you can run without relying on live data or predictions:
- Shared currency check (expected tendency)
- Compute correlation between EUR/USD and EUR/GBP using historical returns.
- Also compute between EUR/USD and GBP/USD. If you see stronger correlation in these comparisons than between unrelated pairs (no shared currency), that supports the shared-currency intuition.
- Time window sensitivity test
- Compute correlation for the same pair pair on two different windows (for example, 1 month vs. 1 year).
- Compare the results. If correlation differs materially, it shows that correlation is not stable.
- Return type check
- Repeat using arithmetic returns vs. log returns.
- If conclusions change, your method is influencing the measured relationship.
These checks help you distinguish “a relationship that often appears” from “a relationship that holds for your measurement choices.”
Limitations and what to verify
Correlation is a descriptive statistic, not a guarantee of future co-movement.
Key limitations:
- Time-varying behavior: correlation can change as volatility, macro news flow, or liquidity conditions change.
- Method dependence: correlation depends on sampling frequency, calculation method, and window length.
- Linear vs non-linear: common correlation measures often capture linear relationships; pairs may move together in more complex ways.
- Shared currency does not ensure strong correlation: two pairs can share a currency but still decouple during specific events.
What you can verify independently:
- Use historical price data for high liquidity pairs.
- Compute correlation on the exact time window and frequency you plan to use.
- Test whether the sign and magnitude persist across multiple windows.
This keeps the conclusion grounded in measurement rather than assumptions.