Bitcoin volatility forecasting with the HAR-type models

Zhang, Jiawei (2019) Bitcoin volatility forecasting with the HAR-type models. [Dissertation (University of Nottingham only)]

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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.

Keywords: bitcoin; realized variance; HAR; signed jumps; volatility forecasting; realized semivariance

Item Type: Dissertation (University of Nottingham only)
Depositing User: Zhang, Jiawei
Date Deposited: 30 Nov 2022 15:45
Last Modified: 30 Nov 2022 15:45

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