Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach

Abstract

The primary purpose of this paper is to investigate whether a novel Markov regime-switching mixed-data sampling (MRS-MIADS) model we design can improve the prediction accuracy of the realized variance (RV) of Bitcoin. Moreover, to verify whether the importance of jumps for RV forecasting changes over time, we extend the standard MIDAS model to characterize two volatility regimes and introduce a jump-driven time-varying transition probability between the two regimes. Our results suggest that the proposed novel MRS-MIDAS model exhibits statistically significant improvement for forecasting the RV of Bitcoin. In addition, we find that jump occurrences significantly increase the persistence of the high-volatility regime and switch between high-and low-volatility regimes. A wide range of checks confirm the robustness of our results. Finally, the proposed model shows significant improvement for 2-week and 1-month horizon forecasts.

Forecasting mid-price movement of Bitcoin futures using machine learning

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“…[ 60 ] and Ma et al. [ 37 ]; we apply recursive (expanding) estimation window to do the evaluation, which can minimize loss of freedom and protect the original data information as much as possible. The results of recursive (expanding) estimation window are presented in Table 10 .…”

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