Implied volatility on options is a gauge of investors’ fears regarding the potential amplitude of future price movements in the underlying commodity or financial instrument. However, implied volatility is rarely evenly distributed. Sometimes, investors fear an upside risk more than a move down. At other times, investors might fear an extreme downside risk more than extreme upside move. The difference between the implied volatility of options with strike prices above and below the current trading price of the underlying instrument is defined as the implied volatility skew.
But what, if anything, does the skew tell us about the likely future direction of prices? For example, if options traders’ price a more extreme upside risk than an extreme downside move, do prices of futures tend to trend higher or lower? In other words, do markets take the cue and trend in the direction of options traders’ greatest fears, move in the opposite direction, or remain unaffected?
The answer, seemingly, depends on the commodity or financial instrument in question. To answer the question, we use CME Group’s new suite of volatility indexes (CVOL). The CVOL index for each commodity or financial futures uses a simple variance methodology that assigns equal weighting to strikes across the entire implied volatility curve. In addition to producing an overall CVOL number covering all strike prices, it also produces UpVol, the implied volatility on options with strikes above the current trading level of the market, and DownVol, the implied volatility on options with strikes below the current trading levels of the market. The difference between these numbers gives us the CVOL skew: UpVol – DownVol = CVOL Skew.
We then construct a diffusion index to normalize the degree of CVOL skew on a scale of zero to 100. For example, if the CVOL skew is the most negative that it has been over the past two years, then it gets a reading of zero. If the CVOL skew is the most positive that it has been over the past two years, it gets a reading of 100. If it’s exactly at its average for the past two years, it would get a reading of 50. (For a detailed discussion on the calculation of the diffusion index, please see the appendix below).
We then evaluate the returns of the futures contract in the three months following each CVOL skew diffusion index observation. For many commodities, including gold, silver, copper, West Texas Intermediate crude oil, ultra-low sulfur diesel (formerly heating oil) and gasoline, there has been a strong negative association between the CVOL skew and subsequent three-month returns in the futures markets. From 2007 to 2023 when traders feared upside risk more than downside volatility, prices tended to fall. When traders feared downside risk more than upside volatility, prices in these markets tended to rise (Figures 1-6). The same is true, albeit to a lesser extent, for certain other markets including soybean meal and the AUDUSD exchange rate (Figures 7 and 8).
For these instruments, the implied volatility skew may be akin to a classic sentiment indicator. When investors fear mainly extreme upside risk, it may have signaled that the market was overbought and therefore more susceptible to declines than gains. By contrast, when CVOL was exceptionally negative in these markets, the opposite often proved to be true: fear of extreme downside risk over extreme upside risk often indicated that markets for these instruments were oversold and susceptible to a bounce in prices.
There is, however, another, set of markets where the opposite has generally been true over the past 16 years. These markets include 5Y, 10Y and 30Y U.S. Treasuries, soybean oil, and to a lesser extent EURUSD, JPYUSD and CADUSD (Figures 9-15). In these markets, the CVOL skew tended, more often than not, to correctly anticipate the future direction of price trends. When traders saw more potential for extreme upside risk than extreme downside risk, these markets tended to trend higher. When investors perceived more extreme downside risk than extreme upside risk, these markets tended to trend lower.
But why would bonds, currency pairs (except for AUDUSD) and soybean oil behave in the opposite way (on balance) with respect to the CVOL skew than markets like metals and energy? For government bond futures markets and for major currency pairs, the answer might lie in the fact that central banks are among the biggest participants in these markets. When central banks begin easing or tightening monetary policy, they tend to keep going in the same direction for a long time – as the most recent tightening cycle has depicts. Moreover, interest rate differentials – and the anticipated evolution of interest rate differentials – between currencies are major drivers of exchange rates. Central banks have a penchant for telegraphing their policy intentions to markets and then acting on them over long periods of time.
Why would AUDUSD behave more like gold, silver, copper, or crude oil/crude oil products? Possibly because AUDUSD may be more heavily influenced by commodity prices, with which it often moves in tandem, than it is by interest rate differentials (Figure 16).
With respect to soybean oil, its behavior is often opposite to that of soybean meal. For example, soybean oil correlates highly with economic growth in China. Soybean meal does not. Soybean oil can also be an additive to fuel blends, especially for diesel, and has sometimes appeared to act as a leading indicator of movements in crude oil prices. Refiners may buy more or less soybean oil as a function of their perceptions for the availability of crude oil in coming months (our article on the soybean oil-crude oil link).
Finally, there are many instruments for which the CVOL skew appears to have had little bearing on future markets returns. This is true for GBPUSD and most of the agricultural complex including corn, wheat and soybeans (Figures 17-20). While CVOL skew does not appear to have had much bearing on the future direction of most crop prices, it is fascinating in a very different way: for all the crop prices, CVOL almost always skews positive. This is to say that out-of-the-money calls almost always cost more than out-of-the-money puts. The reasons for this and its implications, however, are the subject of an upcoming article in our series on CVOL and its uses.
Even those who never trade options might want to pay close attention to the CVOL skew and its potential implications for future movements in spot futures prices. Some assets’ spot futures prices tend to move in the opposite direction of the CVOL skew whereas others tend to trend in the same direction.
The diffusion index is calculated as follows:
- We take the daily CVOL Skew history for the past two calendar years (a total of 500 daily observations). Before CVOL data is available we substitute the highly correlated series 0.15 Delta risk reversal, which is also found in QuikStrike.
- We apply a rank function in a spreadsheet to each daily CVOL skew observation comparing it to previous 499 days + the day itself). For example, if it’s the most negative ever, it gets a zero. If it’s the most positive ever, it gets a 500).
- Next, we use “if/then” statements in a spreadsheet to map the 0 to 500 scale into a 0 to 100 scale. This gives us a matrix of 0s and 1s with each observation falling into one, only one, of the buckets from 0-1 to 99-100.
- Finally, we multiply this matrix through by a vector of the reinvested futures returns over the three-month period after the observation and then divide by the total number of observations in each bucket to give us an average return for each of the 100 buckets in the diffusion index.
CME Group Volatility Index
Learn about how it works, its methodologies, and how it can be a part of your decision-making process.
All examples in this report are hypothetical interpretations of situations and are used for explanation purposes only. The views in this report reflect solely those of the author and not necessarily those of CME Group or its affiliated institutions. This report and the information herein should not be considered investment advice or the results of actual market experience.