This study aims to examine the issue of cryptocurrency volatility modelling and forecasting based on high-frequency data. More specifically, this study assesses whether crisis periods, particularly the coronavirus disease pandemic, influence the dynamic of cryptocurrency volatility. We investigate the four main cryptocurrency markets (Bitcoin, Ethereum Classic, Ethereum, and Ripple) from April 2018 to June 2020. The realized volatility measure is computed and decomposed to various components (continuous versus discontinuous, positive and negative semi-variances, and signed jumps). A variety of heterogeneous autoregressive (HAR) models are developed including these components, thereby enabling assessment of different assumptions (including persistence and asymmetric dynamic) of modelling and volatility forecasting based on in-sample and out-of-sample forecasting strategies, respectively. Our results reveal three main findings. First, the extended HAR model that includes the positive and negative jumps appears to be the best model for predicting future volatility for both crisis and non-crisis periods. Second, during the crisis period, only the negative jump component is statistically significant. Third, in terms of volatility forecasting, the results show that the extended HAR model that includes positive and negative semi-variances outperform the other models.

本研究旨在研究基于高频数据的加密货币波动率建模和预测问题。更具体地说，这项研究评估了危机时期，尤其是冠状病毒疾病大流行，是否会影响加密货币波动的动态。我们调查了 2018 年 4 月至 2020 年 6 月的四个主要加密货币市场（比特币、以太坊经典、以太坊和瑞波币）。 计算实现的波动率度量并将其分解为各种组件（连续与不连续、正和负半方差，并签名跳跃）。开发了各种异构自回归 (HAR) 模型，包括这些组件，从而能够分别基于样本内和样本外预测策略对建模和波动率预测的不同假设（包括持续性和非对称动态）进行评估。我们的结果揭示了三个主要发现。首先，包含正跳和负跳的扩展 HAR 模型似乎是预测危机和非危机时期未来波动性的最佳模型。其次，在危机期间，只有负跳跃成分在统计上是显着的。第三，在波动率预测方面，结果表明，包含正负半方差的扩展 HAR 模型优于其他模型。包含正跳跃和负跳跃的扩展 HAR 模型似乎是预测危机和非危机时期未来波动的最佳模型。其次，在危机期间，只有负跳跃成分在统计上是显着的。第三，在波动率预测方面，结果表明，包含正负半方差的扩展 HAR 模型优于其他模型。包含正跳跃和负跳跃的扩展 HAR 模型似乎是预测危机和非危机时期未来波动的最佳模型。其次，在危机期间，只有负跳跃成分在统计上是显着的。第三，在波动率预测方面，结果表明，包含正负半方差的扩展 HAR 模型优于其他模型。