The Advanced Traders’ Edge: Use CVOL to Decode Complex Treasury Movements

The ability to accurately predict and manage volatility for events like the upcoming election is crucial for traders, especially in the Treasury market. The recent market movements in Treasury futures following a disappointing non-farm payroll number at the beginning of August exemplify the rapid shifts that can occur in interest rates. This event triggered a significant market probability increase of aggressive Federal Open Market Committee (FOMC) rate cuts, underscoring the need for sophisticated tools that can provide traders with a competitive edge.

One such tool that has emerged as a game-changer is the CME Group Volatility Index (CVOL) for Treasuries. CVOL is a suite of multi-asset proprietary volatility indices built upon actively traded options with deep liquidity. CVOL is a turn-key product, delivering advanced volatility calculations and modern analytics that are BMR Benchmark and IOSCO Principles compliant.

Unlike traditional methods, CVOL offers a more nuanced and comprehensive view of volatility by incorporating a wide range of options to gauge market sentiment more accurately. This capability is particularly valuable in a market environment where traditional indicators may lag or provide incomplete insights.

The utility of CVOL was clearly demonstrated during the recent downturn in Treasury rates following the non-farm payroll announcements on the first Fridays of August and September. Traders equipped with CVOL data could quickly assess the shift in market dynamics and adjust their strategies accordingly. This rapid-response capability is essential in a market known for its quick shifts and can mean the difference between capitalizing on opportunities and incurring significant losses.

CVOL is constructed by calculating a variance estimate by utilizing the entire volatility curve, which includes both out-of-the-money (OTM) call and put options for a given expiration. The methodology approximates the area underneath the combined curves of these options by multiplying the option premiums for each strike by the average distance of the adjacent strikes, creating an area that represents the area underneath the curve. These areas are then summed to produce the variance estimate. This estimate is time-weighted with the variance estimate of other expirations to produce a constant 30-day variance estimate, which is then converted into a volatility figure that the CVOL indices represent. This robust construction and depth of option liquidity allows for both live-streaming indices and end-of-day benchmark versions of CVOL, ensuring traders have accurate and timely data to guide their decisions.

Two established methods to calculate volatility are to first calculate log variance or normal variance and take the square root to obtain the standard deviation. Each method describes the distribution of data or observations around a mean.

• Normal variance measures the average squared deviation from the mean, providing a sense of how spread out the data is around the average value. This will provide a more symmetric view on volatility. 

• Log variance transforms the data using a logarithmic function before calculating the variance. When using data that exhibit a high skew in which the average (mean) and middle point of the data (median) are far apart, log variance can increase because it will overweight a direction more often when markets sell off.

CVOL is built using normal variance while other volatility indices use log variance. This provides a consistent value for the entire market underpinning each of the CVOL indices. In addition, CVOL calculation also delivers analytics – UpVar and DownVar, Convexity, Skew and Skew Ratio. These additional five measures provide much more granular specificity to what is driving changes in volatility.

For example, Exhibit 1 shows a large difference between a log variance and normal variance approach illustrated using a period that happened during the onset of the Pandemic. Log variance rose drastically as it captured the one-side move in the market. CVOL’s normal variance also rose, but in a less extreme manner. What was critical was the move in Skew. As yield moved aggressively lower, the volatility on those options increased much more than options underlying high yields.

During such sharp changes in variance levels (high vol-of-vol events), large discrepancies can arise. CVOL’s additional data offers gave traders these additional important insights:

• UpVar - measures the variance of call options

• DownVar - measures the variance of put options

• Skew / Skew Ratio - captures the difference between the variance of calls and puts (i.e. the difference in UpVar and DownVar)

Skew as measured by the difference between variance from UpVar and DownVar can change quite rapidly as the underlying market moves in a specific direction. CVOL Skew data and analytics deliver key insights of what is driving a market, especially with large moves that log variance indices alone do not. For a trader, log variance indices can have dramatic jumps when much of that is related not to greater distribution of the data, but Skew. For risk management purposes, CVOL helps the trader with better tools to decide if they should risk manage with a volatility trade such as a straddle or a Skew trade such as a risk reversal.

The chart above shows the relationship that exists between the measure Skew Ratio and the difference (in vol points) between a log variance calculation and a normal variance calculation for an extended history. What is particularly striking is that large deviations between the two variance calculations is largest when Skew falls, indicating that the difference in pricing is greatest when Skew is lower.

Exhibit 3 shows how CVOL for U.S. 10-year TNote – yield (TYVY) was calculated during the August 2024 nonfarm payroll that sent Treasury volatility up due to the expectation of changing Fed positioning regarding hiking. As can be seen, Skew fell, implying that the corresponding difference between the log variance calculation and the normal variance calculation would have had a large difference.

Viewing the above data, what is of particular note is the divergence in log and normal variance that could have been expected before the large volatility move that happened close to mid-day. There, skew fell but normal variance as measured by CVOL remained level. Log variance in that scenario, as established in previous exhibits, jumped drastically, before falling, only to climb back up again once volatility actually began to rise.

Looking ahead, the upcoming November presidential and congressional elections are poised to introduce further uncertainty into the Treasury market. Historical data suggests that election cycles can lead to increased market volatility, with potential shifts in policy and economic priorities influencing trader sentiment and market dynamics. In addition, as geopolitical risks remain, and the FOMC considers changes in its target rate, the new administration and Congress will need to contend with the debt ceiling at the beginning of 2025.

The integration of CVOL's metrics, such as Skew, into trading strategies will be crucial for understanding asymmetric volatility risks. CVOL indices enable traders to strategize for potential market shifts that are often triggered by macro events. As markets become more volatile, the ability to quickly interpret and react to these changes is vital for minimizing risks and capitalizing on opportunities. Traders can use CVOL across five asset classes with multiple sub indices for advanced and precise volatility risk management.

CVOL is essential for traders aiming to maintain a competitive edge.