Financial markets increasingly use second-order measures to capture risk dynamics. Research and market practice show that volatility-of-volatility — the volatility of a volatility index such as the VIX — can add predictive information about option-implied risks, but its benefits are conditional and nuanced.
What volatility-of-volatility measures capture
Market providers such as Cboe Global Markets publish the VVIX, an index that expresses the market-implied volatility of the VIX. Cboe Global Markets research explains that VVIX reflects option prices on volatility itself and therefore encodes investors’ expectations about future dispersion in implied volatility. Academic work on realized and implied volatility by Torben Andersen at Northwestern University, Tim Bollerslev at Duke University, and Francis X. Diebold at the University of Pennsylvania shows that implied and realized measures often contain complementary information: implied metrics embed risk premia and forward-looking sentiment, while realized metrics capture actual variability. Robert Engle at New York University pioneered models that make the dynamics of volatility tractable, highlighting the importance of capturing time variation in higher moments for forecasting.
Forecasting benefits and limits
Including a volatility-of-volatility measure can improve forecasts of option-implied risks when models are designed to exploit the distinct informational content of second-order moves. Empirical studies and practitioner reports find improved detection of regime shifts and heightened tail-risk signaling during market stress, because large jumps in VVIX often precede sudden widening of option-implied skews. The improvement is not universal: short-term noise, changing liquidity, and model misspecification can erode incremental value. Model complexity also increases, meaning forecasters must guard against overfitting and ensure out-of-sample validation, an emphasis echoed across academic and industry literature.
Causes, consequences and contextual nuance
Causes for elevated volatility-of-volatility include feedback trading, leverage constraints, and concentrated positioning by option market makers; cultural and territorial differences in market structure — for example, differing liquidity across U.S. and emerging markets — shape how strongly VVIX-like signals translate into realized moves. Consequences for risk management include better early warning for tail exposures and more dynamic hedging, but also heavier reliance on models that may fail in stressed or illiquid conditions. Practitioners pairing VVIX with variance risk premium and realized-volatility measures tend to obtain the most robust forecasts, provided they explicitly test for stability and economic interpretability before deployment.