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...
| Main Author: | |
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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/ |
| _version_ | 1848799495062552576 |
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| author | Zhang, Jiawei |
| author_facet | Zhang, Jiawei |
| author_sort | Zhang, Jiawei |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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 |
| first_indexed | 2025-11-14T20:36:34Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-57681 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:36:34Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-576812022-11-30T15:45:42Z https://eprints.nottingham.ac.uk/57681/ Bitcoin volatility forecasting with the HAR-type models Zhang, Jiawei 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 2019-12-01 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/57681/1/4337698%20Finance%20%26%20Investment%20Dissertation%20N14031%20Bitcoin%20volatility%20forecasting%20with%20the%20HAR-type%20models.pdf Zhang, Jiawei (2019) Bitcoin volatility forecasting with the HAR-type models. [Dissertation (University of Nottingham only)] |
| spellingShingle | Zhang, Jiawei Bitcoin volatility forecasting with the HAR-type models |
| title | Bitcoin volatility forecasting with the HAR-type models |
| title_full | Bitcoin volatility forecasting with the HAR-type models |
| title_fullStr | Bitcoin volatility forecasting with the HAR-type models |
| title_full_unstemmed | Bitcoin volatility forecasting with the HAR-type models |
| title_short | Bitcoin volatility forecasting with the HAR-type models |
| title_sort | bitcoin volatility forecasting with the har-type models |
| url | https://eprints.nottingham.ac.uk/57681/ |