FX Volatility Tracker: Buy Short Term EUR/USD Implieds?

Summary

• Short term (1M and 3M) implieds are cheap to realised for EUR/USD and AUD/USD while they are expensive to realised for GBP/USD.
• Short term implieds are fairly valued to realised for USD/JPY.
• We find the volatility curve for EUR/USD suggests buying 1M implieds and selling 1Y implieds.
  
  

Implied vs Realised Volatility – What’s Cheap/Expensive?

We use a range of realised volatility measures and look at realised with and without major events (FOMC, ECB, BoJ, US CPI and NFP days). Our latest update finds the following:

• EUR/USD: 3M implieds remain cheap versus all measures of realised vol (with and without key events, Charts 1 and 2).
• USD/JPY: 1M implieds are fairly valued to realised vol (with and without key events, Tables 3 and 4, Chart 3).
• GBP/USD: 3M implieds are expensive to realised (Chart 1 and Table 5).
• AUD/USD: 3M implieds are cheap to realised vol (Chart 1 and Table 7).

Implied Volatility Slope

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

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:

• EUR/USD: the 1Y/1M slope has typically been inverted (i.e., 1Y lower than 1M) over the past year – however, the slope is currently 1.5 standard deviations above its mean value (Charts 4 and 6). This suggests buying 1M EUR/USD implieds and selling 1Y implieds.
  
  
• USD/JPY: the 6M/3M slope has typically been inverted (i.e., 6M lower than 3M) over the past year but the slope is currently over 1.5 standard deviations above its mean value (Charts 5 and 7). This suggests buying 3M USD/JPY implieds and selling 6M implieds.
  
  
• GBP/USD: The difference between 6M and 3M ATM implieds is 1.1 standard deviations above its mean value over the past year (Charts 8 and 10). This suggests buying 3M GBP/USD implieds and selling 6M implieds.
  
  
• AUD/USD: The difference between 1Y and 6M ATM implieds is 1 standard deviation above its mean value over the past year (Charts 9 and 11). This suggests buying 6M AUD/USD implieds and selling 1Y implieds.

Different Measures of Volatility

Tables 1-8 show the realised volatility (with and without big events) for each currency we track using each of the different estimators along with the corresponding implied volatilities.

Volatility Risk Premium

Tables 9-12 show the volatility risk premium (implied volatility minus realised volatility) for each currency across each of the different estimators. Blue text highlights positive risk premium and red text highlights negative risk premium.

Volatility Risk Premium Percentiles

Tables 13-16 show the 5-year percentiles of the volatility risk premium across each currency, estimator, and period (1 month to 1 year). Blue text highlights values below the 15^th percentile and red text highlights values above 85^th percentile.

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.)