Forecast Bitcoin volatility with least squares model averaging

Tian Xie

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.

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Year of publication:	2019
Authors:	Xie, Tian
Published in:	Econometrics : open access journal. - Basel : MDPI, ISSN 2225-1146, ZDB-ID 2717594-7. - Vol. 7.2019, 3/40, p. 1-20
Subject:	crypto currency \| HAR \| model averaging \| model uncertainty \| volatility forecasting \| Volatilität \| Volatility \| Prognoseverfahren \| Forecasting model \| Virtuelle Währung \| Virtual currency \| Wechselkurs \| Exchange rate \| ARCH-Modell \| ARCH model \| Theorie \| Theory \| Bayes-Statistik \| Bayesian inference