Forecast Bitcoin volatility with least squares model averaging

Artikel

In this paper, we study forecasting problems of Bitcoin-realized volatility computed on data from the largest crypto exchange-Binance. Given the unique features of the crypto asset market, we find that conventional regression models exhibit strong model specification uncertainty. To circumvent this issue, we suggest using least squares model-averaging methods to model and forecast Bitcoin volatility. The empirical results demonstrate that least squares model-averaging methods in general outperform many other conventional regression models that ignore specification uncertainty.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 7 ; Year: 2019 ; Issue: 3 ; Pages: 1-20 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft ; Model Evaluation, Validation, and Selection ; Forecasting Models; Simulation Methods ; Asset Pricing; Trading Volume; Bond Interest Rates ; Financial Forecasting and Simulation
• Subject: crypto currency ; HAR ; model averaging ; model uncertainty ; volatility forecasting

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Xie, Tian
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2019

Further information

• DOI: doi:10.3390/econometrics7030040
• Handle: http://hdl.handle.net/10419/247540
• Last update: 10.03.2025, 11:43 AM CET