Volatility of Volatility: Insights from VVIX

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The volatility of volatility index, VVIX, is a measure of the expected volatility of the VIX index itself. In this newsletter, we will discuss its dynamics, compare it with the VIX index, and explore how it can be used to characterize market regimes.

In this issue:

• Latest Posts

• Dynamics of the Volatility of Volatility Index, VV …

  • Findings

• Using Hurst Exponent on the Volatility of Volatili …

  • Findings

• Closing Thoughts

• Educational Video

  • Can the VVIX Help You Trade?

• Volatility Weekly Recap

• Around the Quantosphere

Latest Posts

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• Low-Volatility Stocks: Reducing Risk Without Sacrificing Returns (11 min)

• The Calendar Effects in Volatility Risk Premium (9 min)

• Stock-Bond Correlation: What Drives It and How to Predict It (10 min)

• Profitability of Dispersion Trading in Liquid and Less Liquid Environments (10 min)

Dynamics of the Volatility of Volatility Index, VVIX

The VVIX, also known as the Volatility of Volatility Index, is a measure that tracks the expected volatility of the CBOE Volatility Index (VIX). As the VIX reflects market participants’ expectations for future volatility in the S&P 500 index, the VVIX provides insights into the market’s perception of volatility uncertainty in the VIX itself.

Reference [1] studied the dynamics of VVIX and compared it to the VIX. 

Findings

• The VVIX tracks the expected volatility of the VIX, providing a direct measure of uncertainty around future changes in market volatility itself.

• It shows strong mean-reverting behavior, indicating that large deviations from its average level tend to reverse over time.

• The VVIX responds asymmetrically to S&P 500 movements, typically increasing more sharply during market downturns than it decreases during upswings.

• It experiences sudden jumps in both directions, reflecting its sensitivity to abrupt changes in market sentiment and conditions.

• A persistent upward trend in the VVIX began well before 2020, driven by factors such as rising VIX volatility and an increasing volatility-of-volatility risk premium (VVRP).

• The growth of the VIX options market from 2006 to 2014 improved liquidity, which likely contributed to the VVIX’s upward trend and closer link to the VIX.

• VVIX and VIX innovations are highly correlated, highlighting their structural connection despite often differing in their responses to specific market events.

• VVIX quickly incorporates new market information, with minimal autocorrelation beyond a single day, showing its responsiveness to real-time market changes.

In summary, this paper analyzes the similarities and differences between the VIX and VVIX, offering key insights for traders and hedgers in the VIX options market. Understanding their relationship helps improve risk management, refine hedging strategies, and better assess market sentiment.

Reference

[1] Stefan Albers, The fear of fear in the US stock market: Changing characteristics of the VVIX, Finance Research Letters, 55

Using Hurst Exponent on the Volatility of Volatility Indices

A market regime refers to a distinct phase or state in financial markets characterized by certain prevailing conditions and dynamics. Two common market regimes are mean-reverting and trending regimes. In a mean-reverting regime, prices tend to fluctuate around a long-term average, with deviations from the mean eventually reverting back to the average. In a trending regime, prices exhibit persistent directional movements, either upwards or downwards, indicating a clear trend.

Reference [2] proposed the use of the Hurst exponent on the volatility of volatility indices in order to characterize the market regime.

Findings

• The study analyzes the volatility of volatility indices using data from five international markets—VIX, VXN, VXD, VHSI, and KSVKOSPI—covering the period from January 2001 to December 2021.

• It employs the Hurst exponent to evaluate long-term memory and persistence in volatility behavior, providing a framework to characterize market regimes over time.

• Different range-based estimators were used to calculate the Hurst exponent on various volatility measures, improving the robustness of the analysis.

• The volatility of volatility indices was estimated through a GARCH(1,1) model, which captures time-varying volatility dynamics effectively.

• The results show that Hurst exponent values derived from volatility of volatility indices reflect market regime shifts more accurately than those from standard volatility indices, supporting the authors’ hypothesis (H1).

• The analysis explores how different trading strategies—momentum, mean-reversion, and random walk—align with the Hurst exponent values, linking theoretical behavior to practical trading outcomes.

• The study highlights the effectiveness of the Hurst exponent as a tool for identifying and interpreting market regimes, which is essential for informed trading and investment decisions.

• Findings are particularly useful for financial analysts and researchers working with volatility indices and market behavior analysis.

• The paper contributes a novel methodological approach by combining Hurst exponent estimation with GARCH modeling and strategy backtesting, offering a comprehensive view of volatility behavior across regimes.

In short, the article highlights the effectiveness of employing the Hurst exponent on the volatility of volatility indices as a suitable method for characterizing the market regime.

Reference

[2] Georgia Zournatzidou and Christos Floros, Hurst Exponent Analysis: Evidence from Volatility Indices and the Volatility of Volatility Indices, J. Risk Financial Manag. 2023, 16(5), 272

Closing Thoughts

In this issue, we explored the dynamics of the VVIX index, and how to use the Hurst exponent on it to characterize the market regime, offering a practical lens through which traders can gauge the persistence or randomness in volatility movements. By understanding these dynamics, market participants can better anticipate shifts in sentiment, enhance their hedging strategies, and adapt more effectively to evolving risk conditions in the options market.

Educational Video

Can the VVIX Help You Trade?

In this video, Tom Sosnoff and Tony Battista dive into market dynamics, focusing on the relationship between the VIX and VVIX. They discuss how traders can use these volatility indices, especially the VVIX, to better understand market sentiment and potential moves. The video also highlights IV Rank as a practical tool for making trading decisions. Along the way, they share useful tips and stories, making complex concepts easier to grasp for traders.

Volatility Weekly Recap

The figure below shows the term structures for the VIX futures (in colour) and the spot VIX (in grey).

Stocks opened the week on a positive note, rising over 2% on news that the White House would delay tariffs against the EU. Overall, by the end of the week, large-cap stocks gained 1.99%, mid-cap stocks gained 1.36%, and small-cap stocks rose 0.92%. Bitcoin and the broader crypto market pulled back. U.S. Treasury yields declined over the week amid renewed trade policy volatility and uncertainty.

In the volatility market, the VIX futures term structure returned to contango, and the roll yield turned positive again. From the chart above, we notice—interestingly—that the roll yield has declined in recent years, as indicated by the dashed black line.

Around the Quantosphere

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• The Billionaire Odd Couple Whose Hedge Fund Is Killing It (wsj)

• Quant finance careers in 2026: More than just banks and hedge funds (tynmagazine)

• Baiont’s Feng Ji: Quant managers who don’t adopt AI will be eliminated by the market (ft)

• Odd Lots: Why Asset Allocators Love Multi-Strategy Hedge Funds (bloomberg)

Disclaimer

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