What “correlate” means for forex pairs
In finance, two forex pairs “correlate” when their returns move together in a measurable way. Correlation is typically expressed as a number between -1 and +1:
- +1: the returns move together perfectly
- 0: no linear co-movement in the chosen data
- -1: they move in opposite directions perfectly
A key limitation is that correlation depends on how you measure it: the time window (days, weeks, months), the return definition (log returns or percentage returns), and the data source. So, “which pairs correlate” cannot be answered as a single fixed list that always holds.
In this article, “high liquidity pairs” refers to the most actively traded major pairs. The practical reason they are discussed together is that they usually have deep markets and widely available data, which makes correlation studies more reproducible across sources.
Direct answer: common correlation patterns among high-liquidity pairs
Within high-liquidity majors, correlation often clusters around shared currency exposure and common macro drivers. Instead of claiming exact results, it is more accurate to describe frequent types of correlation relationships you can test:
1) Pairs that share the same base or quote currency
If two pairs contain the same currency (for example, both include USD), their movements may often be correlated because that currency’s underlying drivers (risk sentiment, interest-rate expectations, broad USD demand) affect both pairs.
Typical examples of “shared currency” comparisons in the major set:
- USD-involving pairs tend to show stronger co-movement with each other than with pairs that do not include USD.
- EUR-involving pairs can show co-movement with each other when the euro’s driver dominates.
- JPY-involving pairs can show co-movement with each other when yen sensitivity to global yields or risk changes dominates.
2) Pairs that are related through cross conversions
Some pairs move in linked ways because one can be expressed using another through currency arithmetic. For instance, EUR/USD and GBP/USD can both be influenced by USD, while EUR/GBP depends on the relative performance between EUR and GBP. That structural linkage means correlations can be higher for pairs that share a dominant “leg,” but the sign and strength can change depending on the market regime.
3) Regime-dependent correlation (risk-on vs risk-off)
High-liquidity pairs can correlate differently across regimes. In periods where a dominant global factor controls moves, correlation across majors can rise. When idiosyncratic factors (like country-specific news) dominate a particular currency, correlations can weaken or flip.
How to check which pairs correlate (and what to compare)
To answer your own “which pairs correlate” question, use a consistent method.
- Pick the universe: high-liquidity majors (a small set of the most traded pair symbols).
- Choose a horizon: correlation can differ for 1-week versus 3-month returns.
- Define returns consistently: for example, compute daily log returns for each pair over the same timestamps.
- Compute correlations: calculate the correlation matrix across pairs.
- Validate stability: re-run the calculation on multiple rolling windows to see whether relationships persist.
A useful independent check is symmetry: if EUR/USD is correlated with USD/JPY in your window, confirm that the relationship is not an artifact of the chosen definition by switching to percentage returns or a different horizon. You should expect some differences.
Comparison criteria: what “similar correlation” can mean
Different studies may produce different ranked “most correlated” pairs because the criteria differ. Common criteria include:
- Correlation by sign: looking for positive correlations only, or considering negative correlations separately. - Strength threshold: treating “high correlation” as above a chosen absolute value. - Out-of-sample stability: checking whether the correlation remains similar beyond the initial window.