FX Volatility Tracker: Introducing Our Core Realised Volatility Measures

Summary

In this report, we add new measures of core (or base) realised volatilities that exclude major event days from their calculation. This complements our five different measures of realised volatility that include all days. Our latest data finds the following:

Using all data including event days

• We find that our measures of realised volatility that incorporate high, low and open data are higher for the 3-month tenor than the standard close-to-close released volatility for EUR/USD, but are lower for USD/JPY, GBP/USD and AUD/USD (Tables 1 to 4).
• Comparing to implieds, we find all major currencies 3-month implieds are below our measures of realised volatility that include all days (Chart 1). For example, 3m EUR/USD implieds at 8.3% is below the realised range of 9.6% to 10.4%.
• Looking at term structure, we find that 1m EUR/USD implieds are higher than realised, while it is lower for the rest of the curve (Chart 2). For 1m USD/JPY implieds, it is within the range of 1m realised, but implieds are lower for the rest of the curve (Chart 3).

Using realised data that excludes event days

• To get a better sense of core (or base) volatility, we strip out big events from our calculations of realised volatility from the last three to fourth months. The big events are FOMC, ECB, BoJ, US CPI and NFP days release days).
• Using these core measures, we find that 3m EUR/USD implieds remain cheap, but 3m USD/JPY implieds are now fairly valued (Charts 4 and 5).

Comparing Different Measures of Volatility Across FX

Looking at the average volatility across each estimator (excluding implied volatility), over each time period, for each currency, we find that EUR/USD is the least volatile over all periods (1 month to 1 year, Table 1). Meanwhile, USD/JPY is most volatile over 1- and 3-month horizons while AUD/USD is most volatile over 6 month and 1-year horizons (Table 2).

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

Comparing 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 Trend

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.

(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.)