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Forecast Bitcoin Volatility with Least Squares Model Averaging

College of Business, Shanghai University of Finance and Economics, Shanghai 200433, China
Econometrics 2019, 7(3), 40; https://doi.org/10.3390/econometrics7030040
Received: 11 July 2019 / Revised: 6 September 2019 / Accepted: 11 September 2019 / Published: 14 September 2019
(This article belongs to the Special Issue Bayesian and Frequentist Model Averaging)
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. View Full-Text
Keywords: volatility forecasting; HAR; model uncertainty; model averaging; crypto currency volatility forecasting; HAR; model uncertainty; model averaging; crypto currency
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Xie, T. Forecast Bitcoin Volatility with Least Squares Model Averaging. Econometrics 2019, 7, 40.

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