In June 2014, the price of oil began to fall from its high of $115 a barrel. The value of the Russian Ruble, as well as the currencies of all major petroleum exporting countries began to drop along with the price of oil. Bloomberg has several correlation modules that allow us to examine the link between market variables. For example, we can quickly explore the relationship between exchange rates and oil prices using Bloomberg’s HRA program.
To plot the Russian Ruble / US Dollar exchange rate against the price of oil in Bloomberg, type: HRA <GO>
This screen shows a regression of the daily Russian Ruble Spot rate with the price of Brent Oil for the period 01/12/2014 through 01/12/2015. Two measures “R” and “R2 “ are useful for interpreting the strength of the correlation. “R” (correlation coefficient) ranges between +1 (total positive correlation) and -1 (total negative correlation). For the Russian Ruble, the R of -.97 indicates a strong negative correlation between the price of oil and the exchange rate of the Ruble against the U.S. Dollar. “R2 “ (the coefficient of determination) indicates the percentage of the change in the Ruble/U.S. Dollar (the dependent variable) that is accounted for by the change in the price of oil (the independent variable). The results of the regression can be downloaded to EXCEL.
In the table below, we show the results of regressions of national currencies for major oil exporters with the price of Brent oil for the period 01/12/2014 through 01/12/2015. China, a major oil importer, is shown as an example of a country whose currency was apparently unaffected by the drop in oil prices.
Another type of correlation program is available through the Bloomberg’s News Trends module (Type NT). NT allows charting of news story counts with historical market data. The news story counts are derived from more than 100 authoritative global sources. This is a useful way to quantify the impact of events (e.g. “Market Sell-Offs”, “Terrorist Attacks” “Epidemics”) on market data. In the graphs below we examine the relation between the appearance of the word “EBOLA” in news stories, and the daily VIX index. The VIX, often referred to as the “investor fear gauge”, is a widely used measure of market risk. There does appear to be a close relationship between the peak of the Ebola scare in October/November 2014 and the movement of the VIX. Obviously much more analysis would have to be done before concluding that the Ebola scare caused the VIX to rise.
Currency rates and oil prices are only two of the thousands of individual securities, indexes, commodities and currencies that can be used in the regression programs. For additional modules dealing with correlations, type Correlation <Help>