We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Forecast Bitcoin Volatility with Least Squares Model Averaging.
- Authors
Xie, Tian
- 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
LEAST squares; BITCOIN; FORECASTING; REGRESSION analysis
- Publication
Econometrics (2225-1146), 2019, Vol 7, Issue 3, p40
- ISSN
2225-1146
- Publication type
Article
- DOI
10.3390/econometrics7030040