FX Volatility Tracker: Realised Measures May Be Overstating Volatility for EUR/USD

Realised Volatility Vs Implied Volatility

We recently extended our measures of realised volatility to include versions that measure core (or base) realised volatilities and exclude major event days from their calculation. This complements our five different measures of realised volatility that include all days.

Using All Data Including Event Days

Our latest data finds the following:

• The conventional measure of realised volatility (close-to-close) could be overstating 3-month volatility for EUR/USD, USD/JPY, GBP/USD, and AUD/USD (Chart 1 and Tables 1 to 4).
• Looking at term structure, for EUR/USD, we find that 1-month to 1-year implieds are all cheaper than our range of realised measures (Chart 2). Elsewhere, we find that 1-month USD/JPY implieds are fairly valued relative to our realised estimators while they remain cheaper relative to realised for the rest of the curve (Chart 3).

Using Realised Data That Excludes Event Days

• Stripping out big events from our calculations can give a better sense of base volatility. We remove big events (FOMC, ECB, BoJ, US CPI and NFP days) from our calculations of realised volatility from the last five to six months.
• Using these base measures, we find that 1-month to 1-year implieds are sit below our range of realised estimators for EUR/USD and USD/JPY (Charts 4 and 5).

Implied Volatility Slope

We analyse the slope of the ATM implied vol curve across tenors ranging from 1-week to 1-year to establish the steepness/flatness of the ATM vol curve.

We also consider Z-scores of the slope values over the past year to establish how extreme the latest values are. Our latest data finds the following:

• The difference between 6M and 3M implieds and between 1Y and 3M implieds for EUR/USD are more than 1.5 standard deviations above their respective mean values over the past year (Chart 6). Furthermore, the slope between 1M and 1W implieds for EUR/USD (21bps) is the largest across all tenors we track while it is smallest between 3M and 1M implieds (-14bps).
• The slope between 1M and 1W implieds for USD/JPY is largest among the tenors we track at 155bps (Chart 7).
• The slope between 6M and 3M implieds for GBP/USD and AUD/USD are both over 1.5 standard deviations above their respective mean values over the past year (Charts 8 and 9).

Different Measures of Volatility Across FX

Tables 1-4 show the realised volatility for each currency we track using each of the different estimators along with the corresponding implied volatilities.

Volatility Risk Premium Across FX

Tables 5-8 show the volatility risk premium (implied volatility minus realised volatility) for each currency across each of the different estimators.

Volatility Risk Premium Percentiles

Tables 9-12 show the 5-year percent rank of the volatility risk premium across each currency, estimator, and time period (1 month to 1 year).

Appendix

We recently published a note comparing different measures of realised volatility. This includes the most common measure of realised volatility – close-to-close volatility (also referred to as historical volatility), which is just the standard deviation of (logarithmic) returns over a historical period.

We also consider range-based volatility estimators that make use of intraday price data (e.g., open, high, low, and close) to derive an estimator of realised volatility that may be more precise and efficient than the standard close-to-close measure.

Notably, we implemented five different measures of realised volatility – Table 13 provides a summary of each.

In this report, we update the estimators with the latest data and look at the volatility risk premium across several currencies we track.

Dalvir Mandara is a Quantitative Researcher at Macro Hive. Dalvir has a BSc Mathematics and Computer Science and an MSc Mathematical Finance both from the University of Birmingham. His areas of interest are in the applications of machine learning, deep learning and alternative data for predictive modelling of financial markets.

Photo Credit: depositphotos.com

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