When it comes to forex, one of the most important things to know is that currencies do not trade in a vacuum. In many cases, foreign economic conditions, interest rates, and price changes affect much more than just a single currency pair. Everything is interrelated in the forex market to some extent and knowing the direction and how strong this relationship is can be an advantage; it has the potential to be a great trading tool. The bottom line is that unless you only want to trade one pair at a time, it can be very profitable to take into account how pairs move relative to one another. To do this, we use correlation analysis. Correlations are calculations based on pricing data, and these numbers can help gauge the relationships that exist between different currency pairs. The information that the numbers provide can be a good aid for any traders who want to diversify their portfolio, double up on positions without investing in the same currency pair, or just get an idea of how much risk their trades are opening them up to. If used correctly, this method has the potential to maximize gains, gauge exposure, and help prevent counterproductive trading.
Knowing how closely correlated the currency pairs are in your portfolio is a great way to measure your exposure and risk. You might think that you're diversifying your portfolio by investing in different pairs, but many of them have a tendency to move in the same or opposite direction from one another. The correlations between pairs can be strong or weak and last for weeks, months, or even years. Basically, what a correlation number measures is an estimate of how often these pairs move together or how opposite their actions are over a specified period of time. Any correlation calculation will be in decimal form; the closer the number is to 1, the stronger the connection between the two currencies. For example, by looking at the sample data in Table 6.1, we can see a +0.94 correlation between the EURUSD and the NZDUSD over the last month. If you are not a fan of decimals, you can also think of the number as a percentage by multiplying it by 100% (in this case, getting a 94% correlation between the EURUSD and the NZDUSD). High decimals reflect currency pairs that closely mirror one another, while lower numbers tell us that the pairs do not usually move in a parallel fashion. Therefore, because there is a high correlation in this particular pair, we can see that by investing in both the EURUSD and the NZDUSD at the same time, you are virtually doubling up on a position. Likewise, it might not be the best idea to go long one of the pairs and short the other because a rally in one has a high likelihood of also setting off a rally in the other currency pair. While this would not make your profit and losses exactly zero because they have different pip values, the two do move in such a similar fashion that taking opposing positions could take a bite out of profits or even cause losses.
Positive correlations aren't the only way to measure similarities between pairings; negative correlations can be just as useful. In this case, instead of a very positive number, we are looking for a highly negative one. The closer the number is to –1, the increasingly connected the two currencies movements are, but this time in the opposite direction. Again, we can use the EURUSD as an example. While we just saw a strong positive correlation with the NZDUSD, the EURUSD has a very negative relationship with the USDCHF. Between these two currency pairs, the correlation has been –0.98 over the last year and –0.99 over the past month. This number indicates that these two pairs have a strong propensity to move in opposite directions. Therefore, buying both currency pairs at the same time will often lead to gains in one and losses in the other. Buying one pair and selling the other would be an intensification of risk that can be viewed as doubling up on the same or similar position.
Anyone who has ever traded the FX market knows that currencies are very dynamic; economic conditions, both sentiment and pricing, change every day. Because of this, the most important aspect to remember when analyzing currency correlations is that they can also change overtime. The strong correlations that are calculated today might not be the same this time next year, or even next month. Due to the constant reshaping of the forex environment, it is imperative to keep current if you decide to use this method for trading. For example, over a one-month period that we observed, the correlation between USDCAD and USDJPY was 0.06. This is a very low number and would indicate that the pairs do not really share any definitive trend in their movements. However, if we look at the three-month data for the same time period, the number increases to 0.12 and then to 0.59 for six months, and finally to 0.80 for a year. In this particular example, we can see that there was a recent breakdown in the relationship between these two pairs. What was once a strongly positive association over time has deteriorated completely in the short term. On the other hand, the correlation between USDCHF and AUDUSD strengthened in more recent reports. The correlation between these two pairs started at –0.78 for the year and edged up to –0.94 for the last month. This suggests that there is an increasing probability that if one of the trades became profitable, the other would as well. The opposite is also true that if one of the trades incurs significant losses, the other has a very high likelihood of also ending up unprofitable.
An even more dramatic example of the extent to which these numbers can change can be found in the GBPUSD and AUDUSD pairs; there was a –0.79 correlation between the two for the yearlong data. However, while these two tended to move in reasonably opposite directions in the long term, over the month of February 2005, for example, they were positively correlated with a +0.76 reading. The major events that change the amount and even direction that pairs are correlated are usually associated with major economic developments, such as interest rate changes or quantitative easing.
Because correlations have the tendency to shift overtime and the data in the table could be stale by the time you read it, the best way to keep current on the direction and strength of your pairings is to calculate them yourself. Although it might seem like a tricky concept, the actual process can be made quite easy. The simplest way to calculate the numbers is to use Microsoft Excel. In Excel, you can take the currency pairs that you want to derive a correlation from over a specific time period and just use the correlation function. Calculating this on a one-year, six-month, three-month, and then on a one-month and six-month trailing basis provides the most comprehensive view of the positive and negative correlation between different currency pairs; however, you can decide which or how many of these readings you want to analyze.
Breaking the process down step by step, let's take a look at how a simple correlation between the USDGBP and USDCHF can be calculated. First, you'll need to get the pricing data for the two pairs. To keep organized, label one column GBP and the other CHF and then put in the daily or weekly values of these currencies using the last price and pairing them with the USD for whatever time period you want to use. At the bottom of the two columns, go to an empty slot and type in =CORREL. Highlight all of the data in one of the pricing columns, type in a comma, and then do the same thing for the other currency; the number produced is your correlation. Although it is not necessary to update your numbers every day, updating them every few weeks or at the very least once a month is generally a good idea.
Figure 6.1 presents a sample of the output results.
| EURUSD | AUDUSD | USDJPY | GBPUSD | NZDUSD | USDCHF | USDCAD | |
| 1 Month | 0.94 | −0.92 | 0.92 | 0.94 | −0.99 | −0.32 | |
| 3 Month | 0.47 | −0.37 | 0.83 | 0.57 | −0.98 | −0.61 | |
| 6 Month | 0.74 | −0.83 | 0.94 | 0.78 | −0.96 | −0.57 | |
| 1 Year | 0.85 | −0 86 | 0.91 | 0.93 | −0 98 | −0.89 | |
| AUDUSD | EURUSD | USDJPY | GBPUSD | NZDUSD | USDCHF | USDCAD | |
| 1 Month | 0.94 | −0.91 | 0.95 | 0.96 | −0 94 | −0.17 | |
| 3 Month | 0.47 | 0.24 | 0.81 | 0.90 | −0 44 | −0.14 | |
| 6 Month | 0.74 | −0.70 | 0.75 | 0.89 | −0.70 | −0.54 | |
| 1 Year | 0.85 | −0.87 | 0.79 | 0.90 | −0.78 | −0.81 | |
| USDJPY | EURUSD | AUDUSD | GBPUSD | NZDUSD | USDCHF | USDCAD | |
| 1 Month | −0.92 | −0.91 | −0.88 | −0.91 | 0.94 | 0.06 | |
| 3 Month | −0.37 | 0.24 | −0.08 | 0.15 | 0.40 | 0.12 | |
| 6 Month | −0.83 | −0.70 | −0.75 | −0.61 | 0.83 | 0.59 | |
| 1 Year | −0.86 | −0.87 | −0.82 | −0.84 | 0.83 | 0.80 | |
| GBPUSD | EURUSD | AUDUSD | USDJPY | NZDUSD | USDCHF | USDCAD | |
| 1 Month | 0.92 | 0.95 | −0.88 | 0.87 | −0.95 | −0.03 | |
| 3 Month | 0.83 | 0.81 | −0.08 | 0.83 | −0.82 | −0.36 | |
| 6 Month | 0.94 | 0.75 | −0.75 | 0.84 | −0.88 | −0.42 | |
| 1 Year | 0.91 | 0.79 | −0.82 | 0.82 | −0.90 | −0.70 | |
| NZDUSD | EURUSD | AUDUSD | USDJPY | GBPUSD | USDCHF | USDCAD | |
| 1 Month | 0.94 | 0.96 | −0.91 | 0.87 | −0.92 | −0.29 | |
| 3 Month | 0.57 | 0.90 | 0.15 | 0.83 | −0.53 | −0.35 | |
| 6 Month | 0.78 | 0.89 | −0.61 | 0.84 | −0.69 | −0.38 | |
| 1 Year | 0.93 | 0.90 | −0.84 | 0.82 | −0.88 | −0.94 | |
| USDCHF | EURUSD | AUDUSD | USDJPY | GBPUSD | NZDUSD | USDCAD | |
| 1 Month | −0.99 | −0.94 | 0.94 | −0.95 | −0.92 | 0.21 | |
| 3 Month | −0.98 | −0 44 | 0.40 | −0.82 | −0.53 | 0.55 | |
| 6 Month | −0.96 | −0 70 | 0.83 | −0.88 | −0.69 | 0.70 | |
| 1 Year | −0.98 | −0 78 | 0.83 | −0.90 | −0.88 | 0.87 | |
| USDCAD | EURUSD | AUDUSD | USDJPY | GBPUSD | NZDUSD | USDCHF | |
| 1 Month | −0.32 | −0.17 | 0.06 | −0.03 | −0.29 | 0.21 | |
| 3 Month | −0.61 | −0.14 | 0.12 | −0.36 | −0.35 | 0.55 | |
| 6 Month | −0.57 | −0.54 | 0.59 | −0 42 | −0.38 | 0.70 | |
| 1 Year | −0.89 | −0.81 | 0.80 | −0 70 | −0.94 | 0.87 | |
| Date | EURUSD | AUDUSD | USDJPY | GBPUSD | NZDUSD | USDCHF | USDCAD |
| 03/29/2004 - 09/29/2004 | 6 Month Trailing | 0.10 | −0.28 | 0.69 | 0.68 | −0.88 | −0.60 |
| 04/29/2004 - 10/28/2004 | 6 Month Trailing | 0.77 | −0.67 | 0.47 | 0.84 | −0.90 | −0.78 |
| 05/31/2004 - 11/29/2004 | 6 Month Trailing | 0.96 | −0.88 | 0.61 | 0.88 | −0.97 | −0.89 |
| 06/30/2004 - 12/29/2004 | 6 Month Trailing | 0.93 | −0.94 | 0.87 | 0.94 | −0.98 | −0.85 |
| 07/30/2004 - 01/28/2005 | 6 Month Trailing | 0.93 | −0.93 | 0.92 | 0.95 | −0.99 | −0.86 |
| 08/31/2004 - 03/01/2005 | 6 Month Trailing | 0.88 | −0.91 | 0.96 | 0.91 | −0.98 | −0.80 |
| 09/30/2004 - 03/31/2005 | 6 Month Trailing | 0.74 | −0.83 | 0.95 | 0.79 | −0.96 | −0.58 |
| Average | 0.76 | −0.78 | 0.78 | 0.86 | −0.95 | −0.77 |
Figure 6.1 March Correlation Table