Bitcoin volatility predictability–The role of jumps and regimes

Lihua Qian, Jiqian Wang, Feng Ma and Ziyang Li

Finance Research Letters, 2022, vol. 47, issue PB

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.

Keywords: Bitcoin volatility; Markov-regime switching; Jump; Mixed data sampling model (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:47:y:2022:i:pb:s1544612322000162

DOI: 10.1016/j.frl.2022.102687

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