Direct answer
In the high-liquidity set of forex pairs, the pairs that are most correlated with each other are usually the ones that share a major currency and respond to the same broad market drivers. In practice, this most often means strong positive correlation among pairs such as EUR/USD, GBP/USD, and AUD/USD (and often USD/JPY with some others depending on the regime). The exact “most correlated” ranking is not universal, because correlations depend on the time period and the correlation method used.
A useful way to bound the question is: when traders say “most correlated,” they usually mean the highest historical correlation of returns over a specific lookback window (for example, daily or hourly changes), computed between two currency pairs.
How correlation between forex pairs works
A forex pair’s price reflects the value of one currency relative to another. When two pairs are correlated, it means their return series tend to move in the same direction (positive correlation) or opposite directions (negative correlation) more often than random chance.
Common inputs:
- Price changes (returns): you compute returns from the pair’s quoted prices over time.
- Lookback window: correlation can differ for 1 month vs. 1 year.
- Correlation metric: typically Pearson correlation on returns, but other measures exist.
Why high-liquidity pairs often correlate:
- Shared currency exposure: pairs that include the same currency (for example, “/USD” pairs) can react similarly to movements in that currency.
- Shared macro drivers: interest-rate expectations, global risk sentiment, and liquidity conditions can affect multiple majors at once.
This is why a “cluster” of major, high-liquidity pairs often moves together more than exotic or less-traded pairs.
Example comparisons and how to verify independently
Without using real-time data, you can still understand what comparisons to run:
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Compare pairs that share USD as the quote or base currency (for example, EUR/USD vs. GBP/USD). If both pairs’ returns often rise and fall together in your chosen window, you’ll see positive correlation.
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Compare pairs that share the opposite side currency (for example, EUR/USD vs. USD/JPY). These may correlate weakly, positively, or negatively depending on how USD strength transmits into JPY and on the current market regime.
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Re-check across multiple periods: compute correlation for at least a few non-overlapping windows. If the “top correlated” pairs change, that indicates correlation is regime-dependent rather than stable.
If you want a starting point for what “high liquidity pairs” means in this context, see high liquidity pairs: /forex/forex-liquidity/high-liquidity-pairs/. For broader context on commonly traded majors, see what are the most popular forex pairs: /forex/forex-liquidity/high-liquidity-pairs/what-are-the-most-popular-forex-pairs/.
Limitations and uncertainty
- Correlation is historical, not a guarantee of future co-movement. Market structure and macro conditions change.
- Method matters: different return frequencies (daily vs. intraday), different calculation methods, and different data cleaning steps can change results.
- Correlation is regime-dependent: during stressed liquidity periods, relationships can shift even among major pairs.
- “Most correlated” is relative: it is a ranking among the pairs you included and the windows you tested.
Because no single set of pairs is always the highest-correlation match across time, the most accurate answer is conditional: among high-liquidity majors, the strongest co-movement is most commonly found among pairs that share major-currency exposure and respond to the same broad drivers, such as the USD-linked major pairs.