Modeling Bitcoin realized volatility by the heterogeneous autoregressive model is subject to substantial model specification uncertainty in practice. To circumvent the lag specification uncertainty, we introduce a new model averaging coefficient estimator with the mean squared error of the coefficient to be minimized. We show that the averaged coefficient vector has a root-$n$ consistency with $n$ being the sample size and propose using a double bootstrap to provide inference. Monte Carlo simulation results demonstrate reliability of the proposed method. The in-sample application shows that adjustment for measurement errors by HARQ-type models is necessary. The model averaging estimator has higher in-sample explanatory power with more significant predictors. The out-of-sample outcomes reveal that the forecast horizon plays a key role at determining the effectiveness of signed realized variance for predicting the Bitcoin volatility. Finally, the model averaging HARQ-type models demonstrate superior out-of-sample performance for both short and long forecast horizons.

在实际中，使用异构自回归模型对比特币实现的波动性进行建模会受到模型规格不确定性的很大影响。为了避免滞后指标的不确定性，我们引入了一种新的模型平均系数估计器，该模型具有最小化系数的均方误差。我们证明平均系数向量的根为$ñ$ 与…一致 $ñ$是样本量，并建议使用双引导程序进行推断。蒙特卡罗仿真结果证明了该方法的可靠性。样本中的应用程序表明，必须通过HARQ类型的模型来调整测量误差。模型平均估算器具有更高的样本内解释能力和更重要的预测因子。样本外结果表明，预测范围在确定已签署的已实现方差对预测比特币波动性的有效性方面起着关键作用。最后，平均的HARQ类型模型的模型在短期和长期预测范围内均表现出优异的样本外性能。