This paper studies volatility cascades across multiple trading horizons in cryptocurrency markets. Using one-minute data on Bitcoin, Ethereum and Ripple against the US dollar, we implement the wavelet Hidden Markov Tree model. This model allows us to estimate the transition probability of high or low volatility at one time scale (horizon) propagating to high or low volatility at the next time scale. We find that when moving from long to short horizons, volatility cascades tend to be symmetric: low volatility at long horizons is likely to be followed by low volatility at short horizons, and high volatility is likely to be followed by high volatility. In contrast, when moving from short to long horizons, volatility cascades are strongly asymmetric: high volatility at short horizons is now likely to be followed by low volatility at long horizons. These results are robust across time periods and cryptocurrencies.

本文研究了加密货币市场中跨多个交易水平的波动级联。我们使用有关美元，比特币，以太坊和瑞波币的一分钟数据，实现了小波隐马尔可夫树模型。该模型使我们能够估计一个时间尺度（水平）上高波动率或低波动率向下一时间尺度上高波动率或低波动率的转移概率。我们发现，从多头到短线波动时，波动率级联趋于对称：长线时的低波动率可能继之以短线时的低波动率，而高波动率则有可能随之而来的是高波动率。相反，当从短距离到长距离移动时，波动率级联是高度不对称的：短距离时的高波动率现在很可能跟随长距离时的低波动率。