**Global FX implied volatility has declined to a historically low level.**

In this paper, we address the following questions: Which currency (or currency bloc) has contributed the most to such a decline and why?

To which extent is global FX volatility linked to volatility in other asset classes (equities, interest rates)? How does the increasing importance of China for international trade impact global FX volatility, and do we see

a greater influence of CNY on currencies of countries with close trade relationships ? If so, to which extent does it dampen global FX realized volatility?

In this paper, we address the following questions: Which currency (or currency bloc) has contributed the most to such a decline and why?

To which extent is global FX volatility linked to volatility in other asset classes (equities, interest rates)? How does the increasing importance of China for international trade impact global FX volatility, and do we see

a greater influence of CNY on currencies of countries with close trade relationships ? If so, to which extent does it dampen global FX realized volatility?

We characterize global FX volatility using the most commonly used market index J.P. Morgan’s global FX implied volatility index. Since index membership and composition are not publicly disclosed, we replicate the index as a linear combination of the one-month implied volatilities of selected currencies. We generate a very good fit to the historical dynamics of the index since 2003 using 12 out of the 28 currencies available

on Refinitiv (CAD, CLP, EUR, GBP, INR, JPY, KRW, MXN, MYR, PHP, SGD and TWD (R-sq= 0.9718; std estimation error = 0.46%)). Our model eliminates currencies whose implied volatilities do not improve the statistical power of our fit (i.e., they share “co-linearities” with other currencies in statistical parlance). For example, in the case of the CHF and the DKK with respect to the EUR or the AUD and the NZD vis-a-vis the CAD.

Figure 1 shows the weights inferred through this exercise (they are all statistically significant). We find that the EUR explains 30.8% of global FX volatility, followed by the CAD (15.8%), the JPY (15.6%) and the SGD (9.5%). Figure 2 then compares the JPM global FX implied volatility index with our estimate. On the basis of these weights, we can also determine that the EUR accounts for 36.6% of the decline in global FX implied volatility observed since the peak of 2008 (45.4% since the end of 2014), followed by the JPY (25.3% and 25.8%).

Figure 1 – Estimated weights in the JPM global FX implied volatility index