Bitcoin volatility forecasting with the HAR-type models - Zhang, Jiawei (2019)
We aim to forecast the volatility of bitcoin using high-frequency data in this paper. Based on the HAR-RV model, we construct extensive HAR-type models by including various jump components and analysis their in-sample regression fitness and out-of-sample forecast abilities. We verify the importance of the jump component in the in-sample regression analysis. The out-of-sample forecast shows that the HAR-RV-SJ and HAR-RV-J models have the best performance. Overall, our result indicates that the HAR-type models have an advantage at mimicking the long memory and using the jump component is significant to the volatility forecast both in-sample and out-of-sample.