Risk of Bitcoin Market: Volatility, Jumps, and Forecasts

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Among all the emerging markets, the cryptocurrency market is considered the most controversial and simultaneously the most interesting one. The visibly significant market capitalization of cryptos motivates modern financial instruments such as futures and options. Those will depend on the dynamics, volatility, or even the jumps of cryptos. In this paper, the risk characteristics for Bitcoin are analyzed from a realized volatility dynamics view. The realized variance RV is estimated with (threshold-)jump components (T)J, semivariance RSV+(−) , and signed jumps (T)J+(−) . Our empirical results show that the Bitcoin market is far riskier than any other developed financial market. Up to 68% of the sample days are identified to entangle jumps. However, the discontinuities do not contribute to the variance significantly. By employing a 90-day rolling-window method, the in-sample evidence suggests that the impacts of predictors change over time systematically under HAR-type models. The out-of-sample forecasting results reveal that the forecasting horizon plays an important role in choosing forecasting models. For long-horizon risk forecast, a finer model calibrated with jumps gives extra utility up to 20 basis points annually, while an approach based on the roughest estimators suits the short-horizon risk forecast best. Last but not least, a simple equal-weighted portfolio not only significantly reduces the size and quantity of jumps but also gives investors higher utility in short-horizon risk forecast case.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IRTG 1792 Discussion Paper ; No. 2019-024

Classification and Topics

• Classification: Wirtschaft ; Forecasting Models; Simulation Methods ; Money and Interest Rates: Forecasting and Simulation: Models and Applications ; Portfolio Choice; Investment Decisions ; Financial Forecasting and Simulation
• Subject: Cryptocurrency ; Bitcoin ; Realized Variance ; Thresholded Jump ; Signed Jumps ; Realized Utility

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Hu, Junjie ; Kuo, Weiyu ; Härdle, Wolfgang Karl
• Event: Veröffentlichung
• (who): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
• (where): Berlin
• (when): 2019

Further information

• Handle: http://hdl.handle.net/10419/230800
• Last update: 10.03.2025, 11:41 AM CET