QDT constructed this study by asking three questions:

1. Can CVOL provide a reliable forecast of whether WTI will rise or fall?
2. Can CVOL help forecast the size of the next WTI price move?
3. Can CVOL help forecast the realized volatility of WTI over the next several sessions?

The analysis used daily WTI crude oil and CVOL data from January 2024 through August 2026. Every feature was aligned to the next session so that only information available at the time of the forecast was used. QDT held back the latest 30% of the sample for out-of-sample testing and used the earlier 70% for model development. The study also applied safeguards for multiple testing and overlapping outcomes.

The result was that CVOL helped describe magnitude and volatility, not direction. The WTI CVOL Index (CLVL) is a 30-day implied volatility calculation built from liquid puts and calls.  CLVL had a 0.429 correlation with the size of the next day’s WTI move. DownVar30 had a 0.637 correlation with realized WTI volatility over the next three sessions. When WTI CVOL was added to the baseline controls, five-day forecast error improved by 3.29%.

These results support CVOL as a market-risk input. They do not support using it as a stand-alone directional signal.

To reduce bias when backtesting static historical data, QDT also ran a walk-forward test that repeatedly trained on earlier observations and evaluated the next unseen period. The resulting error improvements were 1.25% for one day, 1.56% for three days and 3.29% for five days. The five-day result passed five of six validation gates. Its bootstrap confidence interval did not clear zero, however, so it is not yet ready for an automated production weight.

CVOL is most useful when the decision depends on expected move size or market turbulence:

• Traders: Adjust position size, stop distance and profit targets when CVOL points to a wider or narrower trading range.
• Risk managers: Revise risk limits, scenario ranges and stress tests when DownVar rises or falls materially.
• Options traders: Compare implied volatility with QDT’s forecast of realized volatility to evaluate relative value.
• Hedgers: Time or scale protection when the expected size of crude oil moves changes, while using a separate signal for direction.

CVOL should not be used by itself to decide whether WTI will rise or fall. Direction should come from separate evidence such as price trend, supply and demand data, inventory conditions, positioning or a validated directional model. To help forecast directional moves use DataMine Machine Learning Service powered by Quantum Data Technologies (QDT) powerful machine learning platform to quickly and accurately identify what additional datasets can provide directional forecasts. Used together with CVOL, traders can develop models to help forecast direction and risk allocations.

This study covers a limited market period, and volatility relationships can change across regimes. Correlation does not prove causation. Several CVOL components also move together, which can make model coefficients unstable. The five-day improvement is promising but modest, and the bootstrap result means it should be monitored through additional live observations before receiving an automated production weight.

CVOL adds useful forward-looking information about WTI market risk. In QDT’s study, it was most effective for forecasting how large upcoming price moves may be and how volatile the next several sessions may become. Used with a separate directional framework, CVOL can improve position sizing, risk limits, option analysis and hedging decisions.

Sources: CME Group Volatility Indexes (CVOL) overview and methodology; Quantum Data Technologies CVOL/WTI research, August 2026.