Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247540 
Authors: 
Year of Publication: 
2019
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 7 [Issue:] 3 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-20
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
crypto currency
HAR
model averaging
model uncertainty
volatility forecasting
JEL: 
C52
C53
G12
G17
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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