In this paper, we test the role of news in the predictability of return volatility of digital currency market during the COVID-19 pandemic. We use hourly data for cryptocurrencies and daily data for the news indicator, thus, the GARCH MIDAS framework which allows for mixed data frequencies is adopted. We validate the presupposition that fear-induced news triggered by the COVID-19 pandemic increases the return volatilities of the cryptocurrencies compared with the period before the pandemic. We also establish that the predictive model that incorporates the news effects forecasts the return volatility better than the benchmark (historical average)model.

在本文中，我们测试了新闻在 COVID-19 大流行期间数字货币市场收益波动可预测性中的作用。我们使用加密货币的每小时数据和新闻指标的每日数据，因此，采用了允许混合数据频率的 GARCH MIDAS 框架。我们验证了这样的假设：与大流行之前相比，由 COVID-19 大流行引发的恐惧新闻增加了加密货币的回报波动性。我们还建立了包含新闻效应的预测模型比基准（历史平均）模型更好地预测回报波动性。