FX Volatility Tracker: Longer Term EUR/USD Implieds Are Cheap Relative to Realised Volatility

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 slightly overstating 3-month volatility for EUR/USD, GBP/USD, and AUD/USD (Chart 1 and Tables 1 to 4).
  
• Looking at term structure, for EUR/USD, we find that 1-month implieds (7.8%) are fairly valued relative to our range of realised estimators, while implieds are cheaper relative to realised for the rest of the curve (Chart 2). Elsewhere, we find that 1-month USD/JPY implieds (+10.9%) remain higher than our realised range, while implieds remain cheaper relative to realised for the result 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 implieds for EUR/USD (7.8%) and USD/JPY (10.9%) sit within our range of realised estimators, while they remain cheaper relative to realised for the rest of the curve (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. We show the ATM volatility slope on a heatmap where the values correspond to the latest slope value, and the shading corresponds to the Z-score of that slope value over the past year.

Our latest data finds the following:

• The difference between 1Y and 6M ATM implieds is over 1 standard deviation above its mean value over the past year for EUR/USD and GBP/USD (Charts 6, 8, 10, and 12).
• The difference between 6M and 1M ATM implieds is over 1 standard deviation above its mean value over the past year for USD/JPY (Charts 7 and 9).
• The difference between 1Y and 1M ATM implieds is over 1 standard deviation above its mean value over the past year for AUD/USD (Charts 11 and 13).

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

(The commentary contained in the above article does not constitute an offer or a solicitation, or a recommendation to implement or liquidate an investment or to carry out any other transaction. It should not be used as a basis for any investment decision or other decision. Any investment decision should be based on appropriate professional advice specific to your needs.)