In this study, we evaluated the distribution of return on exchange rates of the currencies of G7 countries and also estimated the dynamic effect of Bitcoin transaction prices on currency return in the selected G7 countries. We transformed the daily trading and transaction values of Bitcoin in exchange for the US dollar and exchange rates into continuously compounded daily returns by taking the natural logarithm of today’s exchange rate over yesterday’s rate. We found that the appropriate distribution of returns was the skewed generalized error distribution (SGED). The study invalidates the hypothesis of a normal distribution of returns and rather implies that returns exhibit fat tails. Our study established a significant EGARCH-skewed-GED model effect with substantial asymmetric responsiveness and persistence of conditional volatility of return on foreign exchange rates for the six G7 currencies researched in this study. Our findings show that Bitcoin trading values have considerable predictive power for returns on G7 currency rates. With the EGARCH-SGED model chosen as the best model, it indicates that the error distribution for return is beyond the normal distribution. Accordingly, there are extreme return values that are more common than what would be predicted by a normal distribution. The significance and large positive value of the shape parameter, otherwise called the tail coefficient, signifies heavier tails, while a lower value of the asymmetric coefficient λ signifies slower decay, allowing the distribution to capture extreme return series more effectively. We therefore recommend a downward adjustment of the monetary policy rate to curtail the impact of the negative shocks, namely, bad market news-that snowball volatility in returns. In general, there is a need for overall macroeconomic stabilization.