“FRED” is the St.Louis Federal Reserve Economic Database. It is an excellent repository of economic data, currently boasting 45,000 time-series from 42 data sources. The web-site offers a powerful interface for creating charts of FRED data. Unfortunately, it is a little too powerful, offering a rather dangerous feature: the secondary axis.
I have railed against secondary axes before. They tend to lure the viewer into seeing spurious correlations. Experimenting with FRED, Business Insider has fallen into exactly that trap. In an article entitled “PRESENTING: the ultimate oil currency“, Joe Weisenthal concludes that the euro is surprisingly highly correlated with the price of oil, particularly when oil prices are denominated in gold (OIL.XAU). His evidence is a chart created in FRED (courtesy of the site’s data transformation feature, which allows you to divide the Oil price in US dollars by the price of gold in US dollars).
Wiesenthal goes on to produce similar charts for the Australian dollar (AUD) and the Canadian dollar (CAD), concluding that they do not track the oil price nearly so well. With superimposed time-series like this, the eye is all too easily fooled into seeing correlations which do not exist. Simply separating the lines goes a long way to dispelling this illusion, as the charts below illustrate.
Looking at these charts, the strongest conclusion you would draw is that the euro and the oil price both went up in 2008, with the caveat that the euro started its run somewhat earlier, and the fell again towards the end of the year. At least you would probably agree with Wiesenthal that the Australian and Canadian dollars do not track the price of oil.
Rather than using two axes when comparing financial price histories, it is better to scale both series to a common value (say 100) at an initial point and plot the results against a single axis. Doing this for the euro and the price of oil shows that the rise in oil prices in mid 2008 was far sharper than that of the euro, as was the fall towards the end of the year.
If that chart is not enough to convince you that Wiesenthal’s euro/oil correlation is overblown, perhaps some statistics will help. The absolute price level of the time series is not important. What we need to measure is the correlation of returns (i.e. the percentage change in the prices)*. Daily returns might be a bit noisy, masking any correlations lurking in the data, so I have also calculated correlations for returns over a week (5 trading days) and a month (roughly 20 trading days).
|
1 day Returns |
5 day Returns |
20 day Returns |
| AUD |
35% |
35% |
47% |
| CAD |
-35% |
-34% |
-46% |
| EUR |
20% |
15% |
27% |
Correlation of Returns to OIL.XAU
The correlation between the euro and the oil price is unimpressive, only reaching 27% for monthly returns. Perhaps surprisingly, it is the Australian dollar that shows the highest correlation to oil. Then again, that is probably only surprising after looking at Wiesenthal’s chart. After all, the Australian dollar is known as a “commodity currency”. But even for the Australian dollar, a 47% monthly return correlation for is not very high.
Once again, the lesson here is to beware of secondary axes. If I was running the FRED site, I would ban the feature immediately.
* The problems with computing correlations between serially correlated time series, such as price data, are well known. See for example Granger and Newbold, “Spurious Regressions in Econometrics” (1974).
