Sequential Learning of Cryptocurrency Volatility Dynamics

This paper studies the dynamics of cryptocurrency volatility using a stochastic volatility model with simultaneous and correlated jumps in returns and volatility. We estimate the model using an efficient sequential learning algorithm that allows for learning about multiple unknown model parameters simultaneously, with daily data on four popular cryptocurrencies. We find that these cryptocurrencies have quite different volatility dynamics. In particular, they exhibit different return-volatility relationships: While Ethereum and Litecoin show a negative relationship, Chainlink displays a positive one and interestingly, Bitcoin's one changes from negative to positive in June 2016. We also provide evidence that the sequential learning algorithm helps better detect large jumps in the cryptocurrency market in real time. Overall, incorporating volatility jumps helps better capture the dynamic behavior of highly volatile cryptocurrencies.



Work Title Sequential Learning of Cryptocurrency Volatility Dynamics
Subtitle Evidence Based on a Stochastic Volatility Model with Jumps in Returns and Volatility
Open Access
  1. Jing Zhi Huang
  2. Zhijian James Huang
  3. Li Xu
License In Copyright (Rights Reserved)
Work Type Article
  1. Quarterly Journal of Finance
Publication Date June 1, 2021
Publisher Identifier (DOI)
Deposited November 27, 2021




This resource is currently not in any collection.

Work History

Version 1

  • Created
  • Added HuangHuangXu-Crypto-2021.pdf
  • Added Creator Jing Zhi Huang
  • Added Creator Zhijian James Huang
  • Added Creator Li Xu
  • Published
  • Updated
  • Updated
  • Updated