The file P03_30.xlsx contains monthly data on exchange rates of various currencies versus the U.S. dollar. It is of interest to financial analysts and economists to see whether exchange rates move together through time. You could find the correlations between the exchange rates themselves, but it is often more useful with time series data to check for correlations between differences from month to month. a. Create a column of differences for each currency.
For example, the difference corresponding to Jan-06 will be blank for each currency because the Dec-05 value isn't listed, but the difference for euros in Feb-06 will be 0.8375 0.8247. b. Create a table of correlations between all of the original variables. Then on the same sheet, create a second table of correlations between the difference variables. On this same sheet, enter two cutoff values, one positive such as 0.6 and one negative such as 0.5, and use conditional formatting to color all correlations (in both tables) above the positive cutoff green and all correlations below the negative cutoff red. Do it so that the 1s on the diagonal are not colored. c. Based on the second table and your coloring, can you conclude that these currencies tend to move together in the same direction? If not, what can you conclude? d. Can you explain how the correlation between two currencies like the Chinese yuan and British pound can be fairly highly negatively correlated, whereas the correlation between their differences is essentially zero? Would you conclude that these two currencies "move together?" (Hint: There is no easy answer, but scatterplots and time series graphs for these two currencies and their differences are revealing.)