Forex Correlation Calculator — Currency Pair Correlation Matrix
Instantly look up the correlation between major forex pairs or calculate the Pearson correlation coefficient from your own price series. Understand which pairs move together, which move inversely, and how to use correlation to manage risk and avoid accidental over-exposure in your trading portfolio.
Select the first pair
Pairs with reference data for EUR/USD
Correlation Coefficient
r = 0.89
strong positive
−1.0 (inverse)
0
+1.0 (identical)
Coefficient (r)
0.8900
Strength
strong positive
Pairs compared
EUR/USD / GBP/USD
r = 0.89: Strong positive correlation. The two pairs tend to move in the same direction most of the time. Trading both simultaneously in the same direction doubles your effective exposure.
High Overlap Risk
Note: Static correlations are approximate annual averages. Actual correlations vary over time — recalculate with live data for trading decisions.
Last reviewed · How we check our calculators
How to Use the Forex Correlation Calculator
The calculator has two modes. In Pair Lookup mode, select the first currency pair, then the second from the pairs the reference table has data for with it. The tool returns a pre-computed correlation coefficient based on historical data, along with a strength label and plain-English interpretation.
In Custom Calculation mode, paste two series of prices or returns (comma-separated) into the text boxes — one per currency pair. The tool calculates the Pearson correlation coefficient from your actual data. Use daily closing prices or percentage returns for the same date range on both pairs, so both series have the same number of values. At least 2 data points are required; 20–30 gives a meaningful estimate.
The visual bar shows where the coefficient falls on the −1 to +1 scale. A result near +1 means the pairs move almost identically; near −1 means they move in opposite directions; near 0 means independent movement. Use this information to avoid doubling up on correlated trades and to identify hedging opportunities.
The Formula
The calculator uses the Pearson product-moment correlation coefficient:
- Calculate means: x̄ = mean of series 1, ȳ = mean of series 2
- Numerator: Σ (xᵢ − x̄)(yᵢ − ȳ)
- Denominator: √[ Σ(xᵢ − x̄)² × Σ(yᵢ − ȳ)² ]
- r = Numerator ÷ Denominator
The result r ranges from −1 to +1:
- +0.7 to +1.0: Strong positive correlation — pairs move together
- +0.3 to +0.7: Moderate positive correlation
- −0.3 to +0.3: Weak or no correlation — pairs move independently
- −0.7 to −0.3: Moderate negative correlation
- −1.0 to −0.7: Strong negative correlation — pairs move inversely
If one series is constant (all values identical), the correlation is mathematically undefined and the calculator returns 0. Use at least 20 data points for statistically meaningful results; fewer points produce unstable estimates.
Practical Examples
Example 1 — EUR/USD and GBP/USD (Strongly Correlated)
EUR/USD and GBP/USD historically correlate at approximately +0.89. Both pairs have USD in the denominator and are heavily influenced by USD strength/weakness. If you go long EUR/USD and long GBP/USD simultaneously, you are not spreading risk across two independent trades — you are effectively doubling your USD exposure. A 1% move in USD will impact both positions in the same direction.
Practical implication: If your maximum risk per trade is 1% of your account, and you take full-size positions in both EUR/USD and GBP/USD, your real USD exposure is closer to 2%. Size each position at half your normal size, or choose only one pair.
Example 2 — EUR/USD and USD/CHF (Strongly Inverse)
EUR/USD and USD/CHF correlate at approximately −0.92. CHF is structurally tied to the EUR through Switzerland's trade relationships. When EUR/USD rises (USD weakens), USD/CHF falls (CHF and EUR both strengthen against USD). Going long EUR/USD and long USD/CHF simultaneously is nearly equivalent to a flat trade — the profits from one largely cancel the losses from the other.
Practical implication: This pair combination is useful as a hedge. If you are long EUR/USD and want to reduce but not eliminate your exposure, a partial long in USD/CHF provides a natural offset.
Example 3 — AUD/USD and NZD/USD (Commodity Currency Pair)
AUD/USD and NZD/USD correlate at approximately +0.92. Both are commodity currencies with high exposure to China's economy and risk sentiment. They behave nearly identically in most market conditions, making them poor diversification choices when held together in the same direction.
How Correlation Changes Over Time
Correlations are not static. During risk-off events (financial crises, major geopolitical shocks), correlations between most pairs tend to increase toward +1 as USD or JPY safe-haven flows dominate all markets. During calm periods, correlations can drift lower. Always recalculate correlation using recent data (last 20–60 trading days) rather than relying solely on long-run averages for trading decisions.
Frequently Asked Questions
Forex correlation measures how similarly two currency pairs move over a given period. A correlation of +1 means the pairs move identically; -1 means they move in exactly opposite directions; 0 means they are unrelated. Correlation is measured using the Pearson coefficient calculated from historical price data. Traders use it to understand whether two positions in their portfolio are truly independent risks or whether they are effectively the same bet expressed in different pairs.
EUR/USD and USD/CHF share USD as a common currency, but on opposite sides of the quote. When USD strengthens, EUR/USD falls (it takes fewer USD to buy 1 EUR) while USD/CHF rises (it takes more CHF to buy 1 USD). The Swiss franc is also structurally linked to the euro through Switzerland's deep trade ties with the Eurozone, which reinforces the inverse relationship. This makes the EUR/USD vs USD/CHF correlation one of the most stable and strongly negative correlations in the forex market.
As a rule of thumb, use at least 20–30 observations for a meaningful estimate. Fewer points produce unreliable results — two series with 3 points can show r = 1.0 by coincidence. For trading decisions, 30–60 daily observations (6–12 weeks of data) are commonly used. Some traders prefer 252 observations (1 full trading year) for a longer-term view. Be aware that correlations are unstable and can shift significantly during different market regimes.
Both work, but returns (percentage changes between periods) are statistically preferable. Raw prices of non-stationary time series can produce spurious correlations — two random price series that both trend upward will appear highly correlated even if they are unrelated. Daily percentage returns (or log returns) remove this trend effect and give a more honest picture of the actual relationship. To calculate returns: return = (today's price ÷ yesterday's price) − 1, then multiply by 100 for percentages.
A common threshold is |r| > 0.7. Above this level, the two pairs share more than 49% of their variance (r² > 0.49), meaning there is substantial overlap in what drives each pair. Most risk managers treat pairs with |r| > 0.8 as effectively the same position and apply a combined position size limit. For pairs with 0.5 < |r| < 0.7, holding both is acceptable but position sizes should be scaled down to reflect the partial overlap.
Yes, negatively correlated pairs can partially hedge each other. The approximate hedge ratio is: hedge size = primary position size × |r|. For example, if EUR/USD and USD/CHF have r = −0.92 and you hold 1 lot of EUR/USD long, buying 0.92 lots of USD/CHF long would theoretically offset most of the price risk. In practice, correlations shift and the hedge is imperfect — but it is better than no hedge for short-term risk management. Perfect hedging of a EUR/USD position is better achieved with EUR/USD short positions directly.
The table shows approximate long-run averages based on multi-year historical data. These are useful for understanding structural relationships between pairs, but they drift over time. During risk-off events, most USD-denominated correlations increase. In low-volatility, range-bound markets, correlations tend to fall. For trading decisions, always supplement with a custom calculation using recent data from your preferred time frame rather than relying solely on the static reference values.
Sources
- Pearson correlation coefficient (Wikipedia)
- Kathy Lien, Day Trading and Swing Trading the Currency Market (3rd ed.) (Wiley, 2015)
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