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.

中文翻译：

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加密货币波动动态的序列学习：基于随机波动模型的证据，具有收益和波动的跳跃

本文使用随机波动模型研究加密货币波动的动态，该模型具有同时且相关的收益和波动跳跃。我们使用有效的顺序学习算法估计模型，该算法允许同时学习多个未知模型参数，以及四种流行加密货币的每日数据。我们发现这些加密货币具有完全不同的波动动态。特别是，它们表现出不同的回报-波动率关系：虽然以太坊和莱特币表现出负相关，但 Chainlink 表现出正相关，有趣的是，比特币在 2016 年 6 月从负变为正。我们还提供证据表明顺序学习算法有助于更好实时检测加密货币市场的大幅上涨。全面的，