---
title: Changing VVIX Characteristics in US Stock Market
url: https://www.ml-quant.com/papers/ssrn/4657588/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4657588
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4657588
featured: 2023-12-13
citations: unknown
topic: Derivatives & Volatility
---


# Changing VVIX Characteristics in US Stock Market

The research explores the features of Cboe’s volatility-of-volatility index, showing strong mean reversion, distinct jumps, and a significant upward trend due to higher VIX variation and vol-of-vol risk premium.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4657588
- Identifier: SSRN 4657588
- Released: 2023-03-12
- First featured: Quant Letter No. 29 (2023-12-13): https://www.ml-quant.com/issues/2023-12-13/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [VIX Modeling for Insiders](https://www.ml-quant.com/papers/ssrn/4584019/): The Barndorff-Nielsen Shephard volatility model is expanded to include a jump Ornstein-Uhlenbeck equation with a non-zero stochastic mean-reversion level, offering insights into variance swap pricing.
- [Pricing VIX Derivatives in a Stochastic Volatility Model based on CBI Processes](https://www.ml-quant.com/papers/ssrn/4649270/): The paper introduces a new stock price model based on continuous-state branching processes, providing a formula for VIX put option price.
- [Physics-informed convolutional transformer for predicting volatility surface](https://www.ml-quant.com/papers/arxiv/2209.10771/): The paper presents a new architecture using physics-informed neural networks and convolutional transformers for better predicting financial market volatility.
- [The implied volatility surface (also) is path-dependent](https://www.ml-quant.com/papers/arxiv/2312.15950/): A new forecasting model is suggested that uses past returns and their squares to predict implied volatility surfaces and underlying asset returns for up to two years.
- [Computing Volatility Surfaces using Generative Adversarial Networks with Minimal Arbitrage Violations](https://www.ml-quant.com/papers/arxiv/2304.13128/): The article introduces a generative adversarial network (GAN) method for calculating volatility surfaces, showing that the GAN model is more accurate and faster than artificial neural network (ANN) methods.
- [A Variational Autoencoder Approach to Conditional Generation of Possible Future Volatility Surfaces](https://www.ml-quant.com/papers/ssrn/4628457/): The paper presents a new method for predicting future implied volatility surfaces using historical data, employing a conditional variational autoencoder and a long short-term memory network.