Abstract
This study mainly focuses on the role of jumps in forecasting Bitcoin volatility using linear and nonlinear mixed data sampling models. The results provide strong evidence that using a forecasting model that incorporates continuous-time jump and two-stage regimes can significantly improve predictive accuracy and achieve high economic gains. Interestingly, the superior forecasting ability of the model with a continuous-time jump is reflected in highly volatile periods, especially in the period of a Black Swan event.
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