On Feburary 24th, the Federal Reserve’s Chairman Jerome Powell once again made it clear that interest rates will remain low and the Fed will continue buying bonds to support the U.S. economy. Upon hearing this news, almost anyone with a principles class in Macroeconomics might be compelled to speculate that this may lead to stronger levels of inflation for the US Dollar in the quarters to come, and consequent shifts in exchange rates with stronger currencies.
Whether you believe that the dollar is falling or not, one thing you can do is utilize data science to find out:
Below is an article that can help implementation/speculation: Check out the article below:
Date that article was published: December 23rd, 2020
This article aims to explain that measuring certain trends in currency exchange can be extremely difficult, and that the “stationarity” of a certain time series for a currency determines how easily its future can be decomposed and forecasted using statistical techniques. Of course, the obvious question becomes, how could you possibly measure something like this by hand? Bitter goes on to explain that two specific types of metrics, ADF and KPSS, can indicate the mean, variance and covariance of key data frames in special ways. Using Pandas and R, one can implement these tools and discern whether a particular time series carries a systematic pattern that is unpredictable, or not.
Exchange Rate as of Last week
library(knitr)
stat <- data.frame("Current Dollars to 1 Euro" = 1.22, "52 Week High" = 1.2349)
kable(stat)
| Current.Dollars.to.1.Euro | X52.Week.High |
|---|---|
| 1.22 | 1.2349 |
plot(euro.cross)