Forecast Bitcoin volatility with least squares model averaging - Tian Xie (Econometrics, 2019)
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.
Keywords: C52; C53; G12; G17; 330; crypto currency; HAR; model averaging; model uncertainty; volatility forecasting