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.
Highlights
Bitcoin, the first and still one of the foremost applications of blockchain technology by far, was introduced early in 2008
The small p-values on the heteroskedasticity-robust model averaging HAR method (H-model averaging HAR (MAHAR)) method against other methods, especially that with no model averaging estimators, indicate that the improvement is significant at the 5% level in most cases
We estimate the semi-variance heterogeneous autoregressive (HAR) models in Patton and Sheppard (2015) with the least squares model-averaging method and consider constructing the potential model set with a full permutation of all of the possible lags and the maximum lag order of 30
Summary
The first and still one of the foremost applications of blockchain technology by far, was introduced early in 2008. Audrino et al (2016) found that a conventional fixed lag structure was not statistically sustained by the group LASSO estimates for certain individual stocks in an unstable market environment such as the 2007–2009 crisis They addressed the above issue with a proposed flexible HAR model, built dynamically from the group. The above conclusions may or may not hold in Bitcoin volatility forecasting considering the unique features of the crypto asset market To tackle this question from a different angle, we consider the forecast implication of a flexible lag structure generated by the least squares model-averaging method. We consider the approach designed by Qiu and Xie (2018), who proposed the heteroskedasticity-robust model averaging HAR method (H-MAHAR) that mainly applies the.
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