Bitcoin volatility forecasting with the HAR-type models
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...
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| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2019
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| Online Access: | https://eprints.nottingham.ac.uk/57681/ |
| Summary: | 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 |
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